diff --git a/.gitignore b/.gitignore index 14276ae..4f19644 100644 --- a/.gitignore +++ b/.gitignore @@ -30,6 +30,7 @@ env/ # Testing .pytest_cache/ .coverage +coverage.xml htmlcov/ .tox/ @@ -53,6 +54,7 @@ dist/ # Notebooks .ipynb_checkpoints/ +*.ipynb # OS .DS_Store diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 5701e32..19a2b35 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -43,13 +43,13 @@ repos: language: system types: [python] pass_filenames: false - entry: uv run ruff check src/ configs/ scripts/ --fix --exclude src/leap/_version.py + entry: uv run ruff check src/leap/ configs/ scripts/ --fix --exclude src/leap/_version.py - id: mypy name: Static type checking using mypy language: system types: [python] pass_filenames: false - entry: uv run mypy src/ configs/ --exclude src/leap/_version.py + entry: uv run mypy src/leap/ configs/ --exclude src/leap/_version.py - id: pydoclint name: Docstring linting with pydoclint language: system diff --git a/Makefile b/Makefile index f195de1..e806929 100644 --- a/Makefile +++ b/Makefile @@ -1,4 +1,4 @@ -.PHONY: help install checks testing clean +.PHONY: help install checks tests clean UV_VERSION := 0.8.23 @@ -38,7 +38,7 @@ checks: ## Run pre-commit checks on all files @echo "๐Ÿ” Running checks..." @PIP_INDEX_URL=https://pypi.org/simple PIP_EXTRA_INDEX_URL="" uv run pre-commit run --all-files -testing: ## Run tests with coverage +tests: ## Run tests with coverage @echo "๐Ÿงช Running tests..." @uv run pytest src/tests/ -vv diff --git a/README.md b/README.md index e4456cb..78513c6 100644 --- a/README.md +++ b/README.md @@ -24,7 +24,7 @@ Run quality checks before committing: ```bash make checks # Run pre-commit hooks (linting, formatting, type checking) -make testing # Run tests with coverage +make tests # Run tests with coverage ``` ## Usage diff --git a/badges/cov_badge.svg b/badges/cov_badge.svg index e2dd726..1c769dc 100644 --- a/badges/cov_badge.svg +++ b/badges/cov_badge.svg @@ -1 +1 @@ -coverage: 89%coverage89% +coverage: 58%coverage58% diff --git a/configs/config_perturbation_model.py b/configs/config_perturbation_model.py new file mode 100644 index 0000000..07c6ce1 --- /dev/null +++ b/configs/config_perturbation_model.py @@ -0,0 +1,104 @@ +"""Define perturbation model parameters.""" + +PRED_MODEL_NAME: dict[str, str] = { + "mae_pp_tdnn": "dnn_regressor", + "mae_pp_mlp": "mlp_regressor", + "mae_pp_lgbm": "lgbm_regressor", + "mae_ps_knn": "knn_regressor", + "mae_ps_mlp": "mlp_regressor_small", + "mae_ps_lgbm": "lgbm_regressor_small", + "mae_ps_enet": "elastic_net_regressor", +} + +PRED_MODEL_TYPE: dict[str, str] = { + "mae_pp_tdnn": "pan_perturbation", + "mae_pp_mlp": "pan_perturbation", + "mae_pp_lgbm": "pan_perturbation", + "mae_ps_knn": "multi_label", + "mae_ps_mlp": "perturbation_specific", + "mae_ps_lgbm": "perturbation_specific", + "mae_ps_enet": "perturbation_specific", +} + +RPZ_MODEL_NAME: dict[str, str] = { + "mae_pp_tdnn": "mae", + "mae_pp_mlp": "mae", + "mae_pp_lgbm": "mae", + "mae_ps_knn": "mae", + "mae_ps_mlp": "mae", + "mae_ps_lgbm": "mae", + "mae_ps_enet": "mae", +} + +USE_TRAINED_PREPROCESSOR: dict[str, bool] = { + "mae_pp_tdnn": True, + "mae_pp_mlp": True, + "mae_pp_lgbm": True, + "mae_ps_knn": True, + "mae_ps_mlp": True, + "mae_ps_lgbm": True, + "mae_ps_enet": True, +} + + +USE_TRAINED_RPZ: dict[str, bool] = { + "mae_pp_tdnn": True, + "mae_pp_mlp": True, + "mae_pp_lgbm": True, + "mae_ps_knn": True, + "mae_ps_mlp": True, + "mae_ps_lgbm": True, + "mae_ps_enet": True, +} + +# IMPORTANT: in LEAP we actually use depmap_gdsc_pdx (using all available data) +PRETRAINED_DATA: dict[str, str] = { + "mae_pp_tdnn": "depmap", + "mae_pp_mlp": "depmap", + "mae_pp_lgbm": "depmap", + "mae_ps_knn": "depmap", + "mae_ps_mlp": "depmap", + "mae_ps_lgbm": "depmap", + "mae_ps_enet": "depmap", +} + + +ENSEMBLING: dict[str, bool] = { + "mae_pp_tdnn": True, + "mae_pp_mlp": True, + "mae_pp_lgbm": True, + "mae_ps_knn": True, + "mae_ps_mlp": True, + "mae_ps_lgbm": True, + "mae_ps_enet": True, +} + +ENSEMBLING_SAVE_MODELS_TO_DISK: dict[str, bool] = { + "mae_pp_tdnn": True, + "mae_pp_mlp": True, + "mae_pp_lgbm": True, + "mae_ps_knn": False, + "mae_ps_mlp": False, + "mae_ps_lgbm": False, + "mae_ps_enet": False, +} + +USE_RAY: dict[str, bool] = { + "mae_pp_tdnn": False, + "mae_pp_mlp": False, + "mae_pp_lgbm": False, + "mae_ps_knn": False, + "mae_ps_mlp": True, + "mae_ps_lgbm": True, + "mae_ps_enet": True, +} + +RAY_REMOTE_PARAMS: dict[str, dict | None] = { + "mae_pp_tdnn": None, + "mae_pp_mlp": None, + "mae_pp_lgbm": None, + "mae_ps_knn": None, + "mae_ps_mlp": {"num_cpus": 1, "num_gpus": 0.05}, + "mae_ps_lgbm": {"num_cpus": 8}, + "mae_ps_enet": {"num_cpus": 1}, +} diff --git a/configs/config_regression_model.py b/configs/config_regression_model.py new file mode 100644 index 0000000..b8dd51a --- /dev/null +++ b/configs/config_regression_model.py @@ -0,0 +1,210 @@ +"""Define configs for regression models to use in the pipeline.""" + +from ml_collections import config_dict + +from leap.regression_models import ElasticNet, KnnRegressor, LGBMRegressor, TorchMLPRegressor +from leap.regression_models.utils import AlphaGridElasticNet + + +REGRESSION_MODEL: dict[str, config_dict.ConfigDict] = { + "knn_regressor": config_dict.ConfigDict( + { + "_target_": KnnRegressor, + "n_sample_neighbors": 5, # default + "weights": "uniform", # default + "n_jobs": 30, + } + ), + "elastic_net_regressor": config_dict.ConfigDict( + { + "_target_": ElasticNet, + "l1_ratio": 1.0, + } + ), + "lgbm_regressor": config_dict.ConfigDict( + { + "_target_": LGBMRegressor, + "subsample_for_bin": 400000, + "num_leaves": 4000, + "min_split_gain": 0, + "min_child_weight": 0.01, + "min_child_samples": 5, + "max_depth": 20, + "learning_rate": 0.03, + "reg_lambda": 0, + "reg_alpha": 1, + "colsample_bytree": 0.8, + "n_estimators": 500, + "subsample": 1, + "random_state": 0, + "n_jobs": 50, # launch two in // on large vm + "verbose": -1, + } + ), + "lgbm_regressor_small": config_dict.ConfigDict( + { + # Comment every time the default is changed + "_target_": LGBMRegressor, + "boosting_type": "gbdt", + "num_leaves": 31, + "max_depth": 10, # After small grid, systematically better default is -1 + "learning_rate": 0.01, # TO TUNE, but 0.01 works well. default is 0.1 + "n_estimators": 400, # Default is 100, but 400 is better + "subsample_for_bin": 200000, + "objective": None, + "class_weight": None, + "min_split_gain": 0, + "min_child_weight": 1e-3, + "min_child_samples": (5), # Tuning it is the next best thing to do, default 20 + "subsample": 1, + "subsample_freq": 0, + "colsample_bytree": 0.1, # TO TUNE, much better when small, default is 1.0 + "reg_alpha": 1, # After small grid, better when 1, default is 0 + "reg_lambda": 1, # After small grid, marginally better when 1, default is 0 + "random_state": 0, # for reproducibility + "n_jobs": 8, # small model so we can use less cores + "verbose": -1, # disable prints + } + ), + "mlp_regressor": config_dict.ConfigDict( + { + "_target_": TorchMLPRegressor, + "hidden_layer_sizes": (512, 256, 128, 64, 32, 16), + "activation": "relu", + "learning_rate_init": 0.001, + "max_epochs": 200, + "batch_size": 2048, + "dropout_rate": 0.2, # Best based on tests on 1a-small + "random_seed": 0, + "early_stopping_use": True, + "early_stopping_split": 0.2, + "early_stopping_patience": 20, + "early_stopping_delta": 0.001, + "optimizer_type": "adam", + "weight_decay": 1e-5, + "learning_rate_scheduler": True, # Best based on tests on 1a-small + "scheduler_factor": 0.1, + # If the threshold is the same as the delta, + # this needs to be smaller than the patience of the early stopping + "scheduler_patience": 10, + "scheduler_threshold": 0.001, + "metric": "spearman", + "scaler_name": "robust", + "loss_function_name": "spearman", + } + ), + "mlp_regressor_small": config_dict.ConfigDict( + { + "_target_": TorchMLPRegressor, + "hidden_layer_sizes": (20, 20), + "activation": "relu", + "learning_rate_init": 0.001, + "max_epochs": 200, + "batch_size": 2048, + "dropout_rate": 0.2, # Best based on tests on 1a-small + "random_seed": 0, + "early_stopping_use": True, + "early_stopping_split": 0.2, + "early_stopping_patience": 20, + "early_stopping_delta": 0.001, + "optimizer_type": "adam", + "weight_decay": 1e-5, + "learning_rate_scheduler": True, # Best based on tests on 1a-small + "scheduler_factor": 0.1, + # If the threshold is the same as the delta, + # this needs to be smaller than the patience of the early stopping + "scheduler_patience": 10, + "scheduler_threshold": 0.001, + "metric": "spearman", + "scaler_name": "robust", + "loss_function_name": "spearman", + } + ), + # For the ETL tDNN paper comparison + "dnn_regressor": config_dict.ConfigDict( + { + "_target_": TorchMLPRegressor, + "hidden_layer_sizes": (250, 125, 60, 30), + "activation": "relu", + # "The learning rate was initialized at 0.001" + "learning_rate_init": 0.001, + # "otherwise the full learning process would take 100 epochs" + "max_epochs": 100, + "batch_size": 2048, + "dropout_rate": 0.0, + "random_seed": 0, + # "The learning process would be early stopped if the reduction of + # validation loss was smaller than 0.00001 in 20 epochs" + "early_stopping_use": True, + "early_stopping_split": 0.2, + "early_stopping_patience": 20, + "early_stopping_delta": 0.00001, + # "The Adam optimizer was used with default setting for model learning" + "optimizer_type": "adam", + "weight_decay": 1e-5, + # "The learning rate [...] was reduced by a factor of 10 if the reduction of + # validation loss was smaller than 0.00001 in 10 epochs." + "learning_rate_scheduler": True, + "scheduler_factor": 0.1, + "scheduler_patience": 10, + "scheduler_threshold": 0.00001, + "metric": "mse", # In the ETL paper (tDNN) it's the mse (loss) + "scaler_name": "standard", + "loss_function_name": "mse", + } + ), +} + +HPT_TUNING_PARAM_GRID: dict[str, config_dict.ConfigDict | None] = { + "knn_regressor": None, + "elastic_net_regressor": config_dict.ConfigDict( + { + "alpha": config_dict.ConfigDict( + { + "_target_": AlphaGridElasticNet, + "alpha_min_ratio": 1e-3, + "n_alphas": 10, + } + ), + } + ), + "lgbm_regressor": config_dict.ConfigDict( + { + "reg_alpha": [0, 1], + # log-spaced between 1e-2 and 2e-1, rounded to the first non-zero decimal + "learning_rate": [0.01, 0.02, 0.04, 0.09, 0.2], + } + ), + "lgbm_regressor_small": config_dict.ConfigDict( + { + "learning_rate": [0.005, 0.01], + "colsample_bytree": [0.05, 0.1, 0.15, 0.2, 0.25], + } + ), + "mlp_regressor": config_dict.ConfigDict( + { + # log-spaced between 5e-4 and 1e-2, rounded to the first non-zero decimal + "learning_rate_init": [0.0005, 0.001, 0.002, 0.005, 0.01], + "batch_size": [2048, 8192], + } + ), + "mlp_regressor_small": config_dict.ConfigDict( + { + # log-spaced between 5e-4 and 1e-2, rounded to the first non-zero decimal + "learning_rate_init": [0.0005, 0.001, 0.002, 0.005, 0.01], + "hidden_layer_sizes": [ + (20,), + (20, 20), + ], + } + ), + "dnn_regressor": config_dict.ConfigDict( + { + # This correspond to the HPT done in the ETL paper (tDNN) + # "In the analysis, the dropout rate was selected among 0, 0.1, 0.25, 0.45, + # and 0.7 by minimizing the validation loss. It was the only hyperparameter + # optimized in the model learning process."" + "dropout_rate": [0, 0.1, 0.25, 0.45, 0.7], + } + ), +} diff --git a/configs/config_rpz_model.py b/configs/config_rpz_model.py new file mode 100644 index 0000000..1f53193 --- /dev/null +++ b/configs/config_rpz_model.py @@ -0,0 +1,64 @@ +"""Define configs for rpz models to use in the pipeline.""" + +from ml_collections import config_dict + +from leap.representation_models import PCA, MaskedAutoencoder +from leap.utils import get_device + + +# Detect device (CPU, CUDA, or MPS) +DEVICE = get_device() + +RPZ_MODEL: dict[str, config_dict.ConfigDict] = { + "pca": config_dict.ConfigDict( + { + "_target_": PCA, + "repr_dim": 256, + "random_state": 0, + } + ), + "mae": config_dict.ConfigDict( + { + "_target_": MaskedAutoencoder, + "repr_dim": 256, + "corruption_method": "vime", + "corruption_proba": 0.3, + "hidden_n_units_first": 512, + "hidden_n_layers": 0, + "early_stopping_use": True, + "early_stopping_patience": 20, + "early_stopping_delta": 1e-5, + "max_num_epochs": 1000, + "batch_size": 1024, + "learning_rate": 1e-4, + "retrain": True, + "data_augmentation": False, + "da_noise_std": 0.0, + "dropout": 0.0, + "device": DEVICE, + "random_state": 0, + } + ), + "ae": config_dict.ConfigDict( + { + "_target_": MaskedAutoencoder, + "repr_dim": 256, + "corruption_method": "classic", + "corruption_proba": 0.0, + "hidden_n_units_first": 512, + "hidden_n_layers": 0, + "early_stopping_use": True, + "early_stopping_patience": 100, + "early_stopping_delta": 1e-7, + "max_num_epochs": 10000, + "batch_size": 1024, + "learning_rate": 1e-4, + "retrain": True, + "data_augmentation": False, + "da_noise_std": 0.01, + "dropout": 0.2, + "device": DEVICE, + "random_state": 0, + } + ), +} diff --git a/configs/config_trainer.py b/configs/config_trainer.py new file mode 100644 index 0000000..978b94d --- /dev/null +++ b/configs/config_trainer.py @@ -0,0 +1,89 @@ +"""Define trainer parameters (data and split configs).""" + +from typing import Literal + + +# Data config parameters +SOURCE_DOMAIN_STUDIES: dict[str, str | list[str]] = { + "1": "DepMap_23Q4", + "2": ["GDSC_2020_v2-8_2", "GDSC_2020_v1-8_2", "CCLE_2015", "CTRPv2_2015"], + "3": "DepMap_23Q4", + "4": ["GDSC_2020_v2-8_2", "GDSC_2020_v1-8_2", "CCLE_2015", "CTRPv2_2015"], + "5": ["GDSC_2020_v2-8_2", "GDSC_2020_v1-8_2", "CCLE_2015", "CTRPv2_2015"], +} + +SOURCE_DOMAIN_LABEL: dict[str, str] = { + "1": "gene_dependency", + "2": "AAC", + "3": "gene_dependency", + "4": "AAC", + "5": "AAC", +} + +LIST_OF_PERTURBATIONS: dict[str, str] = { + "1": "perturbations_task_1", + "2": "perturbations_task_2", + "3": "perturbations_task_3", + "4": "perturbations_task_4", + "5": "perturbations_task_5", +} + +FILTER_AVAILABLE_FINGERPRINTS: dict[str, bool] = { + "1": True, + "2": True, + "3": True, + "4": True, + "5": False, +} + +# IMPORTANT: in LEAP we actually use combat_depmap_gdsc_pdx (using combat for batch effect correction) +NORMALIZATION: dict[str, str] = { + "1": "tpm", + "2": "tpm", + "3": "tpm", + "4": "tpm", + "5": "tpm", +} + +# IMPORTANT: in LEAP we actually use most_variant_genes_intersection_depmap_gdsc_pdx +LIST_OF_GENES: dict[str, str] = { + "1": "most_variant_genes", + "2": "most_variant_genes", + "3": "most_variant_genes", + "4": "most_variant_genes", + "5": "most_variant_genes", +} + +TARGET_DOMAIN_STUDIES: dict[str, str | list[str] | None] = { + "1": None, + "2": None, + "3": None, + "4": None, + "5": ["PDXE"], +} + +TARGET_DOMAIN_LABEL: dict[str, str | None] = { + "1": None, + "2": None, + "3": None, + "4": None, + "5": "minus_min_delta_tumor_volume", +} + +# Split config parameters +TEST_SET_TYPE: dict[str, Literal["sample", "perturbation", "tissue", "transfer_learning"]] = { + "1": "sample", + "2": "sample", + "3": "transfer_learning", + "4": "transfer_learning", + "5": "transfer_learning", +} + +# Dimensionality of the fingerprint rpz model +FGPS_DIM: dict[str, int] = { + "1": 256, + "2": 256, + "3": 256, + "4": 256, + "5": 10, # not used +} diff --git a/configs/get_config.py b/configs/get_config.py new file mode 100644 index 0000000..63a9df2 --- /dev/null +++ b/configs/get_config.py @@ -0,0 +1,110 @@ +"""Full trainer config.""" + +import copy +from pathlib import Path + +from ml_collections import config_dict + +from configs import config_perturbation_model, config_trainer +from configs.get_config_data import get_config_data +from configs.get_config_models import get_config_model +from configs.get_config_split import get_config_split + + +REPO_PATH = Path(__file__).parent.parent + + +def get_config( + task_id: str, + model_id: str, + rpz_random_state: int, +) -> config_dict.ConfigDict: + """Create the full trainer configuration. + + Parameters + ---------- + task_id : str + The task id. Possible values are in config_trainer dictionaries. + model_id : str + The model id. Possible values are the keys in the config_perturbation_model dictionaries. For example, + "mae_ps_enet" is the perturbation model_id that uses mae rpz, one model per perturbation, and the enet as a + regression model. + rpz_random_state : int + The random state to use for the RPZ model. + + Raises + ------ + ValueError + If the task_id is not recognized. + If the model_id is not recognized. + If the filter_available_fingerprints is False but a pan-perturbation model is used. + + Returns + ------- + config : config_dict.ConfigDict + The full trainer configuration. + """ + # Extract target tissue name for tasks on target tissues + if task_id[0] in {"3", "4"}: + target_tissue = "_".join(task_id.split("_")[1:]) + task_id = task_id.split("_")[0] + + # Check that the task_id is recognized + if task_id not in config_trainer.SOURCE_DOMAIN_STUDIES: + raise ValueError(f"Task id {task_id} is not recognized.") + + # Check that the model_id is recognized + if model_id not in config_perturbation_model.PRED_MODEL_TYPE: + raise ValueError(f"Model id {model_id} is not recognized.") + + # Get the data configuration + config = get_config_data( + source_domain_studies=config_trainer.SOURCE_DOMAIN_STUDIES[task_id], + source_domain_label=config_trainer.SOURCE_DOMAIN_LABEL[task_id], + list_of_perturbations=config_trainer.LIST_OF_PERTURBATIONS[task_id], + filter_available_fingerprints=config_trainer.FILTER_AVAILABLE_FINGERPRINTS[task_id], + normalization=config_trainer.NORMALIZATION[task_id], + list_of_genes=config_trainer.LIST_OF_GENES[task_id], + target_domain_studies=config_trainer.TARGET_DOMAIN_STUDIES[task_id], + target_domain_label=config_trainer.TARGET_DOMAIN_LABEL[task_id], + ) + + # Split the data into source and target domains for tasks on target tissues + if task_id[0] in {"3", "4"}: + if target_tissue != "": + config.target_domain_data = copy.deepcopy(config.source_domain_data) + # The source data includes all tissues except the target tissue + config.source_domain_data.tissues_to_exclude = [target_tissue] + # The target data includes only the target tissue + config.target_domain_data.tissues_to_keep = [target_tissue] + config.target_domain_data.min_n_label = 0 # enough for test set + + # Get the training/few-shot/test split configuration + config.data_split = get_config_split(test_set_type=config_trainer.TEST_SET_TYPE[task_id]) + + # Define the type of perturbation model + if ( + config_perturbation_model.PRED_MODEL_TYPE[model_id] == "pan_perturbation" + and not config_trainer.FILTER_AVAILABLE_FINGERPRINTS[task_id] + ): + raise ValueError("Filter available fingerprints must be True for a pan-perturbation model.") + + # Get the perturbation model configuration + config.model = get_config_model( + pred_model_type=config_perturbation_model.PRED_MODEL_TYPE[model_id], + pred_model_name=config_perturbation_model.PRED_MODEL_NAME[model_id], + list_of_genes=config_trainer.LIST_OF_GENES[task_id], + normalization=config_trainer.NORMALIZATION[task_id], + rpz_model_name=config_perturbation_model.RPZ_MODEL_NAME[model_id], + use_trained_preprocessor=config_perturbation_model.USE_TRAINED_PREPROCESSOR[model_id], + use_trained_rpz=config_perturbation_model.USE_TRAINED_RPZ[model_id], + pretrained_data=config_perturbation_model.PRETRAINED_DATA[model_id], + rpz_random_state=rpz_random_state, # script argument + fgps_dim=config_trainer.FGPS_DIM[task_id], + ensembling=config_perturbation_model.ENSEMBLING[model_id], + ensembling_save_models_to_disk=(config_perturbation_model.ENSEMBLING_SAVE_MODELS_TO_DISK[model_id]), + use_ray=config_perturbation_model.USE_RAY[model_id], + ray_remote_params=config_perturbation_model.RAY_REMOTE_PARAMS[model_id], + ) + + return config diff --git a/configs/get_config_data.py b/configs/get_config_data.py new file mode 100644 index 0000000..b8cb2fb --- /dev/null +++ b/configs/get_config_data.py @@ -0,0 +1,186 @@ +"""Config for the data to be used in the prediction pipeline.""" + +from pathlib import Path + +from ml_collections import config_dict + +from leap.data.preclinical_dataset import PreclinicalDataset + + +REPO_PATH = Path(__file__).parent.parent + + +def get_config_data( + source_domain_studies: str | list[str], + source_domain_label: str, + list_of_perturbations: str, + filter_available_fingerprints: bool, + normalization: str, + list_of_genes: str, + target_domain_studies: str | list[str] | None = None, + target_domain_label: str | None = None, +) -> config_dict.ConfigDict: + """Create data configuration for the perturbation model trainer. + + Parameters + ---------- + source_domain_studies : str | list[str] + The list of studies to use in the source domain data. + source_domain_label : str + The label to use in the source domain data. Possible values are "gene_dependency", "gene_effect", "AAC", "AUC", + "pIC50", "min_delta_tumor_volume", "minus_min_delta_tumor_volume". + list_of_perturbations : str + The list of perturbations to use. + filter_available_fingerprints : bool + Whether to filter the drugs with available fingerprints. + normalization : str + The normalization to use for rnaseq data. Possible values are "tpm" only. + list_of_genes : str + The list of rnaseq genes to use. + target_domain_studies : str | list[str] | None + The list of studies to use in the target domain data. + target_domain_label : str | None + The label to use in the target domain data. Possible values are "gene_dependency", "gene_effect", "AAC", "AUC", + "pIC50", "min_delta_tumor_volume", "minus_min_delta_tumor_volume". None if no label is used. + + Returns + ------- + config : config_dict.ConfigDict + The data configuration. + """ + # Check arguments + if isinstance(source_domain_studies, str): + source_domain_studies = [source_domain_studies] + if isinstance(target_domain_studies, str): + target_domain_studies = [target_domain_studies] + + # Initialise the configuration + config = config_dict.ConfigDict() + + # Define the source domain data configuration + config.source_domain_data = get_config_preclinical_dataset( + list_of_studies=source_domain_studies, + normalization=normalization, + list_of_genes=list_of_genes, + label=source_domain_label, + list_of_perturbations=list_of_perturbations, + filter_available_fingerprints=filter_available_fingerprints, + ) + + # Define the target domain data configuration + if target_domain_studies is not None: + config.target_domain_data = get_config_preclinical_dataset( + list_of_studies=target_domain_studies, + normalization=normalization, + list_of_genes=list_of_genes, + label=target_domain_label, + list_of_perturbations=list_of_perturbations, + filter_available_fingerprints=filter_available_fingerprints, + ) + else: + config.target_domain_data = None + + return config + + +def get_config_preclinical_dataset( + list_of_studies: list[str], + normalization: str, + list_of_genes: str, + label: str | None, + list_of_perturbations: str | None, + filter_available_fingerprints: bool, +) -> config_dict.ConfigDict: + """Create general preclinical data configuration. + + This configuration can be used to instantiate a PreclinicalDataset object. For parameters description, see the + above docstring. + """ + if len(list_of_studies) != 1 or list_of_studies[0] != "DepMap_23Q4": + raise NotImplementedError("Only DepMap_23Q4 is supported for now.") + + # Define the minimal number of samples per label + if label is None: + min_n_label = 0 + elif label in {"gene_dependency", "gene_effect"}: + min_n_label = 50 + elif label in {"AAC", "AUC", "IC50", "pIC50"}: + if "PRISM_2020" in list_of_studies: + # Use only 15 samples as PRISM is used as an external test dataset + min_n_label = 15 + else: + min_n_label = 75 + elif label in {"min_delta_tumor_volume", "minus_min_delta_tumor_volume"}: + min_n_label = 15 + else: + raise ValueError(f"{label} is not available.") + + # Initialise the configuration + config = config_dict.ConfigDict( + { + "_target_": PreclinicalDataset, + "label": label, + "normalization": normalization, + "scale_label": False, # default value + "min_n_label": min_n_label, + "filter_available_fingerprints": filter_available_fingerprints, + } + ) + + # Define the gene list for rnaseq data + config.use_gene_list = _get_path_list_of_genes_rnaseq(list_of_genes) + + # Define the labels list + if label is not None: + if list_of_perturbations is None: + raise ValueError("list_of_perturbations must be set. if label is not None.") + config.use_label_list = _get_path_list_of_perturbations(list_of_perturbations) + + return config + + +def _get_path_list_of_genes_rnaseq(list_of_genes: str) -> Path | None: + """Get the path to the list of genes to use for rnaseq data.""" + # Return no gene list + possible_gene_lists = {"all", "most_variant_genes"} + + if list_of_genes not in possible_gene_lists: + raise ValueError( + f"{list_of_genes} does not exis. Please provide a valid `list_of_genes` in {possible_gene_lists}." + ) + + if list_of_genes == "all": + return None + + # Return the selected gene list + file_path = REPO_PATH / "data" / f"list_of_{list_of_genes}.csv" + if file_path.exists(): + return file_path + + raise NotImplementedError(f"{list_of_genes} cannot be found. Please add the file {file_path} to data/.") + + +def _get_path_list_of_perturbations(list_of_perturbations: str) -> Path | None: + """Get the path to the list of perturbations to use.""" + possible_perturbations = { + "all", + "perturbations_task_1", + "perturbations_task_2", + "perturbations_task_3", + "perturbations_task_4", + "perturbations_task_5", + } + if list_of_perturbations not in possible_perturbations: + raise ValueError( + f"{list_of_perturbations} does not exis. Please provide a valid " + f"`list_of_perturbations` in {possible_perturbations}." + ) + + if list_of_perturbations == "all": + return None + + file_path = REPO_PATH / "data" / f"list_of_{list_of_perturbations}.csv" + if file_path.exists(): + return file_path + + raise NotImplementedError(f"{list_of_perturbations} cannot be found. Please add the file {file_path} to data/.") diff --git a/configs/get_config_models.py b/configs/get_config_models.py new file mode 100644 index 0000000..67e73ea --- /dev/null +++ b/configs/get_config_models.py @@ -0,0 +1,331 @@ +"""Config for the one model per perturbation to be used in the prediction pipeline.""" + +from pathlib import Path + +from loguru import logger +from ml_collections import config_dict + +from configs import config_regression_model, config_rpz_model +from leap.data.preprocessor import OmicsPreprocessor +from leap.data.splits import cv_split_generator +from leap.pipelines.perturbation_pipeline import PerturbationPipeline +from leap.representation_models import PCA + + +# Define paths for pretrained models (relative to repository root) +REPO_PATH = Path(__file__).parent.parent +PREPROCESSOR_PATH = REPO_PATH / "models" / "preprocessors" +RPZ_PATH = REPO_PATH / "models" / "rpz" + + +def get_config_model( + pred_model_type: str, + pred_model_name: str, + list_of_genes: str, + normalization: str, + rpz_model_name: str, + use_trained_preprocessor: bool = False, + use_trained_rpz: bool = False, + pretrained_data: str = "depmap", + rpz_random_state: int = 42, + fgps_dim: int = 500, + ensembling: bool = True, + ensembling_save_models_to_disk: bool = False, + use_ray: bool = False, + ray_remote_params: dict | None = None, +) -> config_dict.ConfigDict: + """Set the model configuration. + + Parameters + ---------- + pred_model_type : str + The type of model to use. Possible values are: "multi_label", "perturbation_specific", or "pan_perturbation". + pred_model_name : str + Name of the prediction model. + list_of_genes : str + The list of genes used in the trained preprocessor and rpz for rnaseq. + normalization : str, optional + The normalization method to use for rnaseq data, by default "tpm". + rpz_model_name : str, optional + The name of the trained rpz to use for rnaseq data, by default pca. + use_trained_preprocessor : bool, optional + Whether to use a trained preprocessor for RNASeq that is already saved, by default False. + use_trained_rpz : bool, optional + Whether to use a trained rpz for RNASeq that is already saved, by default False. + pretrained_data : str, optional + The name of the pretrained data to use, by default "depmap". + rpz_random_state : int, optional + The random state to use for the rpz model, by default 42. + fgps_dim : int, optional + The dimension of the fingerprint rpz model, by default 500. + ensembling : bool, optional + Whether to use ensembling, by default True. + ensembling_save_models_to_disk : bool, optional + Whether to save the ensembling models to disk, this can save RAM and prevent out of memory errors for heavy + models, by default False. + use_ray : bool, optional + Whether to use Ray to parallelise over the perturbations. Only possible if one_model_per_perturbation is True. + Default is False. + ray_remote_params : dict | None, optional + Parameters for Ray remote. Defaults to None. + + Raises + ------ + ValueError + If pred_model_type is not one of ("multi_label", "perturbation_specific", + "pan_perturbation"). + + Returns + ------- + config : config_dict.ConfigDict + The model configuration. + """ + # temp, using the rnaseq form 23Q4 + if use_ray and pred_model_type != "perturbation_specific": + logger.error("Ray can only be used for one model per perturbation.") + if pred_model_type == "multi_label": + return get_config_one_model_all_perturbations_multi_label( + pred_model_name=pred_model_name, + list_of_genes=list_of_genes, + normalization=normalization, + rpz_model_name=rpz_model_name, + use_trained_preprocessor=use_trained_preprocessor, + use_trained_rpz=use_trained_rpz, + pretrained_data=pretrained_data, + rpz_random_state=rpz_random_state, + ensembling=ensembling, + ensembling_save_models_to_disk=ensembling_save_models_to_disk, + ) + if pred_model_type == "perturbation_specific": + return get_config_one_model_per_perturbation( + pred_model_name=pred_model_name, + list_of_genes=list_of_genes, + normalization=normalization, + rpz_model_name=rpz_model_name, + use_trained_preprocessor=use_trained_preprocessor, + use_trained_rpz=use_trained_rpz, + pretrained_data=pretrained_data, + rpz_random_state=rpz_random_state, + fgps_dim=fgps_dim, + ensembling=ensembling, + ensembling_save_models_to_disk=ensembling_save_models_to_disk, + use_ray=use_ray, + ray_remote_params=ray_remote_params, + ) + if pred_model_type == "pan_perturbation": + return get_config_one_model_all_perturbations_single_label( + pred_model_name=pred_model_name, + list_of_genes=list_of_genes, + normalization=normalization, + rpz_model_name=rpz_model_name, + use_trained_preprocessor=use_trained_preprocessor, + use_trained_rpz=use_trained_rpz, + pretrained_data=pretrained_data, + rpz_random_state=rpz_random_state, + fgps_dim=fgps_dim, + ensembling=ensembling, + ensembling_save_models_to_disk=ensembling_save_models_to_disk, + ) + raise ValueError(f"Invalid pred_model_type: {pred_model_type}") + + +def _config_backbone( + pred_model_name: str, + list_of_genes: str, + normalization: str, + rpz_model_name: str, + use_trained_preprocessor: bool, + use_trained_rpz: bool, + pretrained_data: str, + rpz_random_state: int, + fgps_dim: int, + ensembling: bool, + ensembling_save_models_to_disk: bool, +) -> config_dict.ConfigDict: + """Generate the part of the config which is common to all model configs. + + Please refer to the get_config_model docstrings for parameter descriptions. + """ + # Initialise a default perturbation model config + config = config_dict.ConfigDict( + { + "_target_": PerturbationPipeline, + "ensembling": ensembling, + "ensembling_save_models_to_disk": ensembling_save_models_to_disk, + "fgpt_rpz_model": config_dict.ConfigDict( + { + "_target_": PCA, + "repr_dim": fgps_dim, + } + ), + "use_ray": False, + } + ) + + config.hpt_tuning_cv_split = config_dict.ConfigDict( + { + "_target_": cv_split_generator, + "_partial_": True, + "k_fold": True, + "group_variable": None, + "leave_one_group_out": False, + "test_split_ratio": None, + "n_splits": 5, + "random_state": 0, + } + ) + + # Define preprocessor + if use_trained_preprocessor: + config.preprocessor_model_rnaseq = PREPROCESSOR_PATH / ( + f"log_mean_std_{pretrained_data}_{list_of_genes}_{normalization}_seed_{rpz_random_state}.pkl" + ) + else: + config.preprocessor_model_rnaseq = config_dict.ConfigDict( + { + "_target_": OmicsPreprocessor, + "scaling_method": "mean_std", + "max_genes": -1, + "gene_list_source": None, + "log_scaling": True, # log-scaling is typically done during normalization + } + ) + + # Define rpz model + if rpz_model_name is None or rpz_model_name == "identity": + config.rpz_model_rnaseq = None + elif use_trained_rpz: + config.rpz_model_rnaseq = RPZ_PATH / ( + f"{rpz_model_name}_{pretrained_data}_{list_of_genes}_{normalization}_seed_{rpz_random_state}.pkl" + ) + else: + config.rpz_model_rnaseq = config_rpz_model.RPZ_MODEL[rpz_model_name] + config.rpz_model_rnaseq.random_state = rpz_random_state + + # Define the regression model configuration + config.regression_model_base_instance = config_regression_model.REGRESSION_MODEL[pred_model_name] + config.hpt_tuning_param_grid = config_regression_model.HPT_TUNING_PARAM_GRID[pred_model_name] + + return config + + +def get_config_one_model_per_perturbation( + pred_model_name: str, + list_of_genes: str, + normalization: str, + rpz_model_name: str, + use_trained_preprocessor: bool, + use_trained_rpz: bool, + pretrained_data: str, + rpz_random_state: int, + fgps_dim: int, + ensembling: bool, + ensembling_save_models_to_disk: bool, + use_ray: bool, + ray_remote_params: dict | None, +) -> config_dict.ConfigDict: + """Model configuration for one model per perturbation (perturbation-specific models). + + Please refer to the get_config_model docstrings for parameter descriptions. + """ + config = _config_backbone( + pred_model_name=pred_model_name, + list_of_genes=list_of_genes, + normalization=normalization, + rpz_model_name=rpz_model_name, + use_trained_preprocessor=use_trained_preprocessor, + use_trained_rpz=use_trained_rpz, + pretrained_data=pretrained_data, + rpz_random_state=rpz_random_state, + fgps_dim=fgps_dim, + ensembling=ensembling, + ensembling_save_models_to_disk=ensembling_save_models_to_disk, + ) + + # Define the tuning metric + config.hpt_tuning_score = "auc" if "classifier" in pred_model_name else "spearman" + config.one_model_per_perturbation = True + + # Use Ray to parallelise over perturbations + config.use_ray = use_ray + config.ray_remote_params = ray_remote_params + + return config + + +def get_config_one_model_all_perturbations_single_label( + pred_model_name: str, + list_of_genes: str, + normalization: str, + rpz_model_name: str, + use_trained_preprocessor: bool, + use_trained_rpz: bool, + pretrained_data: str, + rpz_random_state: int, + fgps_dim: int, + ensembling: bool, + ensembling_save_models_to_disk: bool, +) -> config_dict.ConfigDict: + """Model configuration for a pan-perturbation model (single label per sample). + + Please refer to the get_config_model docstrings for parameter descriptions. + """ + config = _config_backbone( + pred_model_name=pred_model_name, + list_of_genes=list_of_genes, + normalization=normalization, + rpz_model_name=rpz_model_name, + use_trained_preprocessor=use_trained_preprocessor, + use_trained_rpz=use_trained_rpz, + pretrained_data=pretrained_data, + rpz_random_state=rpz_random_state, + fgps_dim=fgps_dim, + ensembling=ensembling, + ensembling_save_models_to_disk=ensembling_save_models_to_disk, + ) + + # Define the tuning metric + config.hpt_tuning_score = "auc" if "classifier" in pred_model_name else "spearman" + config.one_model_per_perturbation = False + + # Group by sample for pan-perturbation models + config.hpt_tuning_cv_split.group_variable = "sample" + return config + + +def get_config_one_model_all_perturbations_multi_label( + pred_model_name: str, + list_of_genes: str, + normalization: str, + rpz_model_name: str, + use_trained_preprocessor: bool, + use_trained_rpz: bool, + pretrained_data: str, + rpz_random_state: int, + ensembling: bool, + ensembling_save_models_to_disk: bool, +) -> config_dict.ConfigDict: + """Model configuration for a pan-perturbation model with multi-labels. + + This framework is adapted for multilabel prediction (e.g., KNN regressor). + Memory and time efficient compared to the perturbation-specific framework. + + Please refer to the get_config_model docstrings for parameter descriptions. + """ + config = _config_backbone( + pred_model_name=pred_model_name, + list_of_genes=list_of_genes, + normalization=normalization, + rpz_model_name=rpz_model_name, + use_trained_preprocessor=use_trained_preprocessor, + use_trained_rpz=use_trained_rpz, + pretrained_data=pretrained_data, + rpz_random_state=rpz_random_state, + fgps_dim=0, # Not used for multi-label models + ensembling=ensembling, + ensembling_save_models_to_disk=ensembling_save_models_to_disk, + ) + config.one_model_per_perturbation = False + config.hpt_tuning_score = None + config.hpt_tuning_cv_split = None + return config diff --git a/configs/get_config_split.py b/configs/get_config_split.py new file mode 100644 index 0000000..5e38466 --- /dev/null +++ b/configs/get_config_split.py @@ -0,0 +1,146 @@ +"""Config for the training/few-shot/test splits to be used in the trainer.""" + +from typing import Literal + +from ml_collections import config_dict + +from leap.data.splits import cv_split_ids + + +def get_config_split( + test_set_type: Literal["sample", "perturbation", "tissue", "transfer_learning"], training_split_count: int = -1 +) -> config_dict.ConfigDict: + """Create configuration for the training/few-shot/test splits. + + Parameters + ---------- + test_set_type : Literal["sample", "perturbation", "tissue", "transfer_learning"] + Type of test set to use. Possible values include: + - "sample" for evaluation in unseen sample (cell lines or patients), + - "perturbation" for evaluation in unseen perturbations, + - "tissue" for evaluation in unseen tissues, + - "transfer_learning" for evaluation in target data (unseen tissue, pdx or patient). + training_split_count : int, optional + Number of samples in the training set. Only used if test_set_type is "transfer_learning". + + Raises + ------ + NotImplementedError + If the test_set_type is not supported. + + Returns + ------- + config : config_dict.ConfigDict + Configuration for the data split. + """ + if test_set_type == "sample": + return _get_config_split_unseen_sample() + if test_set_type == "perturbation": + return _get_config_split_unseen_perturbation() + if test_set_type == "tissue": + return _get_config_split_unseen_tissue() + if test_set_type == "transfer_learning": + return _get_config_split_transfer_learning(training_split_count) + raise NotImplementedError(f"Test set type {test_set_type} is not supported.") + + +def _get_config_split_unseen_sample() -> config_dict.ConfigDict: + """Create configuration for test in unseen cell lines.""" + return _get_config_cv_split_ids( + group_variable="sample", + subgroup_variable=None, + stratify_variable=None, + test_split_ratio=0.2, + training_split_count=None, + n_splits=10, + ) + + +def _get_config_split_unseen_perturbation() -> config_dict.ConfigDict: + """Create configuration for test in unseen perturbations.""" + return _get_config_cv_split_ids( + group_variable="perturbation", + subgroup_variable=None, + stratify_variable=None, + test_split_ratio=0.2, + training_split_count=None, + n_splits=10, + ) + + +def _get_config_split_unseen_tissue() -> config_dict.ConfigDict: + """Create configuration for test in unseen tissues.""" + return _get_config_cv_split_ids( + group_variable="sample", + subgroup_variable=None, + stratify_variable=None, + test_split_ratio=1.0, + training_split_count=None, + n_splits=1, + ) + + +def _get_config_split_transfer_learning(training_split_count: int = -1) -> config_dict.ConfigDict: + """Create configuration for test in unseen disease models.""" + training_split_count = 10 if training_split_count < 0 else training_split_count + return _get_config_cv_split_ids( + group_variable=None, + subgroup_variable="perturbation", + stratify_variable=None, + test_split_ratio=None, + training_split_count=training_split_count, + n_splits=100, + ) + + +def _get_config_cv_split_ids( + group_variable: str | None, + subgroup_variable: str | None, + stratify_variable: str | None, + test_split_ratio: float | None, + training_split_count: int | None, + n_splits: int, +) -> config_dict.ConfigDict: + """Create general configuration for cross-validation splits. + + The cv_split_ids function used to generate the splits takes as input the stacked sample metadata where rows + correspond to sample x perturbation pairs. The group_variable indicates if the split should be done by sample or by + perturbation. + + Parameters + ---------- + group_variable : str | None + Column name in X_metadata to use for groups. + subgroup_variable : str | None + Column name in X_metadata to use for splitting. + stratify_variable : str | None + Column name in X_metadata to use for stratification. + test_split_ratio : float | None + Ratio of the test set size over the total number of sample x perturbation pairs. This should be between 0 and 1. + training_split_count : int | None + Number of samples in the test set. Only used if test_split_ratio is None. + n_splits : int + Number of splits generate. + + Returns + ------- + config : config_dict.ConfigDict + Configuration for the data split. + """ + return config_dict.ConfigDict( + { + "_target_": cv_split_ids, + "_partial_": True, + "k_fold": False, # never used for our training / test splits + "group_variable": group_variable, + "subgroup_variable": subgroup_variable, + "stratify_variable": stratify_variable, + "leave_one_group_out": False, # never used + "test_split_ratio": test_split_ratio, + "training_split_count": training_split_count, + "n_splits": n_splits, + "n_min_loo": None, # never used + "list_test_groups": None, # never used + "random_state": 0, + } + ) diff --git a/coverage.xml b/coverage.xml deleted file mode 100644 index 966a393..0000000 --- a/coverage.xml +++ /dev/null @@ -1,42 +0,0 @@ - - - - - - /Users/gdissez/Documents/code/leap - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/data/README.md b/data/README.md index f228954..9a06ed5 100644 --- a/data/README.md +++ b/data/README.md @@ -2,4 +2,13 @@ Directory for storing datasets. +To run experiments, please download the following files from [DepMap](https://depmap.org/portal/data_page/?tab=allData): +- CRISPRGeneDependency.csv +- OmicsExpressionTPMLogp1HumanProteinCodingGenes.csv +- Model.csv + +And [this file](https://www.gsea-msigdb.org/gsea/msigdb/download_file.jsp?filePath=/msigdb/release/2025.1.Hs/c2.all.v2025.1.Hs.json) from MsigDB. This file is the JSON bundle associated with the GCP (chemical and genetic perturbations) gene set. + +Save all those file in this directory. + **Note:** Data files are gitignored. Only this README is tracked. diff --git a/data/processed/.gitkeep b/data/processed/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/pyproject.toml b/pyproject.toml index 4648303..5146a1b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,10 +3,10 @@ build-backend = "hatchling.build" requires = ["hatchling", "hatch-vcs"] [tool.hatch.build.targets.sdist] -include = ["README.md", "src/leap"] +include = ["README.md", "src/leap", "configs"] [tool.hatch.build.targets.wheel] -packages = ["src/leap"] +packages = ["src/leap", "configs"] [tool.hatch.version] source = "vcs" @@ -57,7 +57,15 @@ dependencies = [ "numpy>=2.0.0", "pandas>=2.0.0", "scikit-learn>=1.3.0", + "scipy>=1.16.0", "torch>=2.0.0", + "loguru>=0.7.0", + "skglm<0.4", # Breaking changes introduced in the 0.4 version in Apr 25 + "ray>=2.20.0", + "tqdm>=4.0.0", + "lightgbm>=4.1.0", + "pyarrow>=19.0.0", + "ml-collections>=1.1.0", ] [project.urls] @@ -172,7 +180,7 @@ convention = "numpy" python_version = "3.11" ignore_errors = false files = ["src/", "configs/", "scripts/"] -mypy_path = ["src", "configs", "scripts"] +mypy_path = ["."] # Enforce typing on public functions disallow_incomplete_defs = true @@ -188,15 +196,28 @@ strict_equality = true [tool.coverage.run] branch = true -source_pkgs = ["leap"] +source_pkgs = ["leap", "configs"] [tool.coverage.report] omit = [] exclude_lines = [ "pragma: no cover", "if TYPE_CHECKING:", + "import ", + "from .* import ", + "@abstractmethod", + "@abc.abstractmethod", + "if __name__ == .__main__.:", + "raise NotImplementedError", + "raise AssertionError", + "def __repr__", + "def __str__", + "__all__", ] +[tool.pydoclint] +should-document-star-arguments=false + [tool.pytest.ini_options] testpaths = ["src/tests"] python_files = ["test_*.py", "*_test.py"] @@ -209,6 +230,7 @@ addopts = [ "--cov-report=term-missing", "--cov-report=html", "--cov-report=xml", + "-p", "no:threadexception", # Prevent segfaults with PyTorch tests ] markers = [ "slow: marks tests as slow (deselect with '-m \"not slow\"')", diff --git a/scripts/__init__.py b/scripts/__init__.py new file mode 100644 index 0000000..6e2d2e4 --- /dev/null +++ b/scripts/__init__.py @@ -0,0 +1 @@ +"""Scripts for training models.""" diff --git a/scripts/train_and_save_preprocessor_and_rpz.py b/scripts/train_and_save_preprocessor_and_rpz.py new file mode 100644 index 0000000..6c34401 --- /dev/null +++ b/scripts/train_and_save_preprocessor_and_rpz.py @@ -0,0 +1,82 @@ +"""Train the rnaseq preprocessor and rpz to be used in the prediction pipeline.""" + +# %% +from pathlib import Path + +from loguru import logger + +from configs.config_rpz_model import RPZ_MODEL +from leap.data.preclinical_dataset import PreclinicalDataset +from leap.data.preprocessor import OmicsPreprocessor +from leap.utils.config_utils import instantiate +from leap.utils.io import save_pickle + + +MODELS_PATH = Path(__file__).parent.parent / "models" + + +# %% +def train_preprocessor_and_rpz( + studies: list, + rpz_model_name: str, + list_of_genes: str = "most_variant_genes", + normalization: str = "tpm", + random_seed: int = 0, +): + """Train the rnaseq preprocessor and rpz.""" + # Define output file names + output_path_preprocessor = ( + MODELS_PATH + / "preprocessors" + / (f"log_mean_std_{'_'.join(studies)}_{list_of_genes}_{normalization}_seed_{random_seed}.pkl") + ) + output_path_rpz = ( + MODELS_PATH + / "rpz" + / (f"{rpz_model_name}_{'_'.join(studies)}_{list_of_genes}_{normalization}_seed_{random_seed}.pkl") + ) + + # Load the data + gene_list = Path(__file__).parent.parent / "data" / f"list_of_{list_of_genes}.csv" + X = PreclinicalDataset(label=None, normalization=normalization, use_gene_list=gene_list).df_rnaseq + + preprocessor = OmicsPreprocessor( + scaling_method="mean_std", + max_genes=-1, + log_scaling=("combat" not in normalization), # log-scaling is done before combat + ) + X.columns = X.columns.str.replace("_rnaseq", "", regex=False) + preprocessor.fit(X=X) + + # Save the trained preprocessor + save_pickle(preprocessor, output_path_preprocessor) + + # Transform the data using the trained preprocessor + df_rnaseq_transformed = preprocessor.transform(X=X) + df_rnaseq_transformed.columns = df_rnaseq_transformed.columns + "_rnaseq" + + # Define the rpz model config + config_rpz_model = RPZ_MODEL[rpz_model_name] + config_rpz_model.random_state = random_seed + + # Fit the rpz model + logger.info("Fitting RPZ...") + rpz_full = instantiate(config_rpz_model) + rpz_full.fit(df_rnaseq_transformed) + # Save the trained RPZ + save_pickle(rpz_full, output_path_rpz) + + +# %% +if __name__ == "__main__": + for random_seed in range(5): + logger.info(f"Training preprocessor and mae with seed {random_seed}...") + train_preprocessor_and_rpz( + studies=["depmap"], + rpz_model_name="mae", + list_of_genes="most_variant_genes", + normalization="tpm", + random_seed=random_seed, + ) + +# %% diff --git a/src/leap/data/__init__.py b/src/leap/data/__init__.py new file mode 100644 index 0000000..47e1014 --- /dev/null +++ b/src/leap/data/__init__.py @@ -0,0 +1 @@ +"""Data module for LEAP.""" diff --git a/src/leap/data/load_depmap.py b/src/leap/data/load_depmap.py new file mode 100644 index 0000000..27bda43 --- /dev/null +++ b/src/leap/data/load_depmap.py @@ -0,0 +1,61 @@ +"""Loading functions for depmap data.""" + +import re +from pathlib import Path + +import numpy as np +import pandas as pd +from loguru import logger + + +DATA_PATH = Path(__file__).parent.parent.parent.parent / "data" + + +def load_expression() -> pd.DataFrame: + """Load DepMap RNASeq data with tpm normalization. + + It is important to note that RNAseq tpm data from DepMap is already log-scaled with log2(X+1), therefore here we + convert the tpm data with exp2(x) - 1 so that we can access the TPM values. + """ + path_processed = DATA_PATH / "processed" / "tpm_rnaseq_processed.parquet" + if path_processed.exists(): + depmap_expr = pd.read_parquet(path_processed) + else: + logger.info("Preprocessing DepMap RNASeq data with tpm normalization...") + path = DATA_PATH / "OmicsExpressionTPMLogp1HumanProteinCodingGenes.csv" + depmap_expr = ( + pd.read_csv(path, index_col=0) + .drop(columns=["SequencingID", "IsDefaultEntryForModel", "ModelConditionID", "IsDefaultEntryForMC"]) + .rename(columns={"ModelID": "DepMap_ID"}) + .set_index("DepMap_ID") + .apply(lambda x: np.exp2(x) - 1) + .astype("float32") + ) + # Clean column names to have gene symbol only + depmap_expr.rename(columns=lambda x: re.sub(r"[\(\[ ].*?[\)\]]", "", x), inplace=True) + depmap_expr.to_parquet(path_processed, engine="pyarrow", compression="brotli") + return depmap_expr + + +def load_essentiality() -> pd.DataFrame: + """Load DeepDEP essentiality scores.""" + labels_path_processed = DATA_PATH / "processed" / "dependencies_processed.parquet" + labels_path_raw = DATA_PATH / "CRISPRGeneDependency.csv" + + if labels_path_processed.exists(): + gene_dependencies = pd.read_parquet(labels_path_processed) + else: + logger.info("Preprocessing DepMap CRISPR data...") + gene_dependencies = pd.read_csv(labels_path_raw, index_col=0).rename( + columns=lambda x: re.sub(r"[\(\[ ].*?[\)\]]", "", x) + ) + gene_dependencies.index.names = ["DepMap_ID"] + gene_dependencies.to_parquet(labels_path_processed, engine="pyarrow", compression="brotli") + return gene_dependencies + + +def load_metadata() -> pd.DataFrame: + """Load metadata for all of DepMap's cancer cell lines.""" + df_sample_info = pd.read_csv(DATA_PATH / "Model.csv", index_col=0) + df_sample_info.index.names = ["DepMap_ID"] + return df_sample_info diff --git a/src/leap/data/load_gene_sets.py b/src/leap/data/load_gene_sets.py new file mode 100644 index 0000000..b43042f --- /dev/null +++ b/src/leap/data/load_gene_sets.py @@ -0,0 +1,73 @@ +"""Loading functions for MSigDB gene set data.""" + +import json +from pathlib import Path + +import pandas as pd +from loguru import logger + + +DATA_PATH = Path(__file__).parent.parent.parent.parent / "data" + + +def load_fingerprints() -> pd.DataFrame: + """Load MSigDB gene set membership matrix as fingerprints. + + Returns a binary DataFrame where rows are genes, columns are gene set names, + and values are 1 if the gene belongs to the gene set, 0 otherwise. + + The data is cached as a processed parquet file for faster loading. + + Returns + ------- + pd.DataFrame + Binary membership matrix with shape (n_genes, n_gene_sets). + Index: gene symbols + Columns: gene set names + Values: 1 (gene in set) or 0 (gene not in set) + """ + path_processed = DATA_PATH / "processed" / "gene_set_fingerprints_processed.parquet" + + if path_processed.exists(): + fingerprints = pd.read_parquet(path_processed) + else: + logger.info("Processing MSigDB gene sets to create fingerprint matrix...") + path = DATA_PATH / "c2.cgp.v2025.1.Hs.json" + + # Load raw JSON data + with open(path) as f: + raw_data = json.load(f) + + logger.info(f"Loaded {len(raw_data)} gene sets") + + # Collect all unique genes + all_genes: set[str] = set() + for gene_set_data in raw_data.values(): + all_genes.update(gene_set_data["geneSymbols"]) + + all_genes_list = sorted(all_genes) # Sort for consistent ordering + gene_set_names = list(raw_data.keys()) + + logger.info(f"Creating membership matrix with {len(all_genes_list)} genes and {len(gene_set_names)} gene sets") + + # Create binary matrix efficiently + data = {gene_set_name: [0] * len(all_genes_list) for gene_set_name in gene_set_names} + gene_to_idx = {gene: idx for idx, gene in enumerate(all_genes_list)} + + for gene_set_name, gene_set_data in raw_data.items(): + for gene in gene_set_data["geneSymbols"]: + idx = gene_to_idx[gene] + data[gene_set_name][idx] = 1 + + # Create DataFrame + fingerprints = pd.DataFrame(data, index=all_genes_list) + fingerprints.index.name = "gene" + + # Save to parquet for faster loading next time + path_processed.parent.mkdir(parents=True, exist_ok=True) + fingerprints.to_parquet(path_processed, engine="pyarrow", compression="brotli") + + logger.info(f"Created and cached fingerprint matrix with shape {fingerprints.shape}") + logger.info(f"Matrix density: {fingerprints.sum().sum() / (fingerprints.shape[0] * fingerprints.shape[1]):.2%}") + + return fingerprints diff --git a/src/leap/data/preclinical_dataset.py b/src/leap/data/preclinical_dataset.py new file mode 100644 index 0000000..ef9cfa5 --- /dev/null +++ b/src/leap/data/preclinical_dataset.py @@ -0,0 +1,379 @@ +"""A standardised class for preclinical datasets.""" + +from typing import Any + +import numpy as np +import pandas as pd +from loguru import logger +from sklearn.preprocessing import StandardScaler + +from leap.data.load_depmap import load_essentiality, load_expression, load_metadata +from leap.data.load_gene_sets import load_fingerprints + + +class PreclinicalDataset: + """A preclinical dataset. + + A preclinical dataset including RNAseq and gene essentiality data from DepMap. Can be extended to other datasets, + modalities and labels. + + Parameters + ---------- + label : str | None, optional + The label to load. Possible values are "gene_dependency". Default is "gene_dependency". + normalization : str, optional + The normalization method to use for RNAseq data. Currently only "tpm" is supported. Default is "tpm". + scale_label : bool, optional + Whether to scale a continuous label, by default False. + min_n_label : int, optional + The minimum number of non-missing labels to keep, by default 50. + use_label_list : str | None, optional + Path to the list of labels to use, by default None. + use_gene_list : str | None, optional + Path to the list of genes to use for RNAseq, by default None (using all available genes). + filter_available_fingerprints : bool, optional + Whether to keep only the labels with available fingerprints, by default False. + tissues_to_keep : str | list[str], optional + The tissues to keep, by default "all". + tissues_to_exclude : str | list[str] | None, optional + The tissues to exclude, by default None. + + Attributes + ---------- + df_rnaseq : pd.DataFrame + RNAseq expression data for cell lines. + df_sample_metadata : pd.DataFrame + Metadata for cell lines. + df_labels : pd.DataFrame + Gene essentiality labels (samples x genes). + df_fingerprints : pd.DataFrame | None + Fingerprints for genes (if available). + df_labels_stacked : pd.DataFrame + Stacked labels (sample x perturbation pairs). + df_sample_metadata_stacked : pd.DataFrame + Sample metadata aligned with stacked labels. + + Raises + ------ + ValueError + If the normalization method is not supported. + """ + + def __init__( + self, + label: str | None = "gene_dependency", + normalization: str = "tpm", + scale_label: bool = False, + min_n_label: int = 50, + use_label_list: str | None = None, + use_gene_list: str | None = None, + filter_available_fingerprints: bool = False, + tissues_to_keep: str | list[str] = "all", + tissues_to_exclude: str | list[str] | None = None, + ) -> None: + self.label = label + self.normalization = normalization + self.scale_label = scale_label + self.min_n_label = min_n_label + self.use_label_list = use_label_list + self.use_gene_list = use_gene_list + self.filter_available_fingerprints = filter_available_fingerprints + self.tissues_to_keep = tissues_to_keep + self.tissues_to_exclude = tissues_to_exclude + + # Validate normalization method + if self.normalization != "tpm": + raise ValueError(f"Only 'tpm' normalization is currently supported, got '{self.normalization}'.") + + # Define attributes + self.df_labels: pd.DataFrame + self.df_labels_stacked: pd.DataFrame + self.df_sample_metadata: pd.DataFrame + self.df_sample_metadata_stacked: pd.DataFrame + self.df_fingerprints: pd.DataFrame | None + self.df_rnaseq: pd.DataFrame + + # Load metadata + self._load_sample_metadata() + + # Load labels if specified + if self.label is not None: + self._load_labels() + self._load_fingerprints() + else: + self.df_labels = pd.DataFrame(index=self.df_sample_metadata.index) + self.df_fingerprints = None + + # Load RNAseq + self._load_rnaseq() + if self.label is not None: + logger.info(f"Loaded {self.df_labels.shape[1]} perturbations in {self.df_labels.shape[0]} samples.") + if self.df_fingerprints is not None: + logger.info(f"Loaded fingerprints for {self.df_fingerprints.shape[0]} perturbations.") + + # Filter samples based on tissue + self._filter_tissues() + # Align sample info with labels and rnaseq + self.align_sample_data() + # Sort rows and columns to ensure consistency + self._sort_rows_and_columns() + # Use float64 for rnaseq data + self._format_dataframe() + # Stack labels and align sample metadata + self.stack_dataframes() + + def __setstate__(self, state: dict[str, Any]) -> None: + """Set the state of the object. + + This method is called when unpickling an object, and is customized to handle + the renaming of attributes. + """ + # Handle None dataframes + df_keys = [ + "df_labels", + "df_labels_stacked", + "df_sample_metadata", + "df_sample_metadata_stacked", + "df_fingerprints", + "df_rnaseq", + ] + for key in df_keys: + if key in state and state[key] is None: + del state[key] + self.__dict__.update(state) + + def stack_dataframes(self) -> None: + """Stack labels and align sample metadata.""" + # Stack labels such that rows correspond to sample x perturbation pairs + if self.label is not None: + stacked_labels = self.df_labels.stack() + stacked_labels.index.names = ["sample", "perturbation"] + self.df_labels_stacked = pd.DataFrame(stacked_labels, columns=["label"]) + + # Align a stacked sample metadata dataframe + self.df_sample_metadata_stacked = self.df_labels_stacked.merge( + self.df_sample_metadata, left_on="sample", right_index=True + )[self.df_sample_metadata.columns] + self.df_sample_metadata_stacked = pd.merge( + self.df_sample_metadata_stacked, self.df_labels_stacked, on=["sample", "perturbation"], how="inner" + ) + + # Create sample and perturbation columns to make them more accessible + self.df_sample_metadata_stacked["sample"] = self.df_sample_metadata_stacked.index.get_level_values("sample") + self.df_sample_metadata_stacked["perturbation"] = self.df_sample_metadata_stacked.index.get_level_values( + "perturbation" + ) + self.df_sample_metadata_stacked["perturbation_label"] = ( + self.df_sample_metadata_stacked["perturbation"] + + "_" + + self.df_sample_metadata_stacked["label"].astype(str) + ) + + def _load_sample_metadata(self) -> None: + """Load metadata on the samples from DepMap.""" + df_sample_metadata = load_metadata() + logger.info(f"Loaded metadata for {df_sample_metadata.shape[0]} cell lines.") + + # Rename tissue column to match expected format + if "OncotreeLineage" in df_sample_metadata.columns: + df_sample_metadata = df_sample_metadata.rename(columns={"OncotreeLineage": "tissue"}) + + self.df_sample_metadata = df_sample_metadata + + def _load_rnaseq(self) -> None: + """Load RNAseq data from DepMap.""" + df_rnaseq = load_expression() + logger.info( + f"Loaded expression of {df_rnaseq.shape[1]} genes in {df_rnaseq.shape[0]} cell lines " + f"(normalization: {self.normalization})." + ) + + # Keep only the listed genes if specified + self._use_gene_list(df_rnaseq) + # Add rnaseq suffix + self.df_rnaseq = df_rnaseq.add_suffix("_rnaseq") + + def _load_labels(self) -> None: + """Load gene essentiality labels from DepMap.""" + if self.label == "gene_dependency": + df_labels = load_essentiality() + else: + raise ValueError(f"Invalid label: {self.label}. Only 'gene_dependency' is supported for DepMap.") + + # Standardise perturbation names + df_labels.columns = pd.Index([name.lower() for name in df_labels.columns]) + + # Remove any infinite values + df_labels.replace([-np.inf, np.inf], np.nan, inplace=True) + + if self.scale_label: + scaler = StandardScaler() + df_labels = pd.DataFrame(scaler.fit_transform(df_labels), columns=df_labels.columns, index=df_labels.index) + + # Keep only the listed labels + self._use_label_list(df_labels) + # Keep only samples with at least one label + df_labels.dropna(how="all", inplace=True) + self.df_labels = df_labels + + def _load_fingerprints(self) -> None: + """Load fingerprints for genes from DepMap.""" + df_fingerprints = load_fingerprints() + # Standardise perturbation names to match labels + df_fingerprints.index = pd.Index([name.lower() for name in df_fingerprints.index]) + # Keep only fingerprints for genes in df_labels + common_genes = list(set(df_fingerprints.index).intersection(set(self.df_labels.columns))) + self.df_fingerprints = df_fingerprints.loc[common_genes] + + # Filter labels to only keep those with available fingerprints if requested + if self.filter_available_fingerprints: + self.df_labels = self.df_labels.loc[:, common_genes] + logger.info(f"Filtered labels to keep only {len(common_genes)} genes with available fingerprints.") + + logger.info(f"Loaded fingerprints for {self.df_fingerprints.shape[0]} genes.") + + def _use_label_list(self, df_labels: pd.DataFrame) -> None: + """Only keep the listed labels.""" + if self.use_label_list is not None: + label_list = list(pd.read_csv(self.use_label_list, header=None).iloc[:, 0]) + label_list = [name.lower() for name in label_list] + df_labels = df_labels.loc[:, df_labels.columns.isin(label_list)] + self.df_labels = df_labels + + def _use_gene_list(self, df_rnaseq: pd.DataFrame) -> None: + """Only keep the listed RNASeq genes.""" + if self.use_gene_list is not None: + # Load the gene list from the specified file + gene_list_rnaseq = pd.read_csv(self.use_gene_list, header=None).iloc[:, 0].str.removesuffix("_rnaseq") + df_rnaseq = df_rnaseq.loc[:, df_rnaseq.columns.isin(gene_list_rnaseq)] + + def _get_common_samples(self, *dfs: pd.DataFrame) -> list[str]: + """Get the common samples between dataframes.""" + common_samples = set(dfs[0].index) + for df in dfs[1:]: + if df is not None: + common_samples.intersection_update(df.index) + return sorted(common_samples) + + def align_sample_data(self) -> None: + """Align sample metadata with labels and rnaseq data. + + This function ensures that df_rnaseq, df_labels, and df_sample_metadata have the same index. + We consider two strategies: + - If self.min_n_label > 0, we only keep samples with at least min_n_label + labels. This is useful for training prediction models. + - If self.min_n_label == 0, we keep all samples with rnaseq data. This is useful + for training RPZ models. + """ + if self.min_n_label == 0: + self.df_labels = self.df_labels.reindex(self.df_rnaseq.index) + + common_samples = self._get_common_samples( + self.df_sample_metadata, + self.df_labels, + self.df_rnaseq, + ) + + logger.info( + f"{len(self.df_rnaseq) - len(common_samples)} rnaseq samples are" + " dropped when aligning with labels and metadata." + ) + logger.info( + f"{len(self.df_labels) - len(common_samples)} labelled samples are dropped when aligning with metadata." + ) + logger.info( + f"{len(self.df_sample_metadata) - len(common_samples)} metadata" + " samples are dropped when aligning with labels." + ) + + self.df_sample_metadata = self.df_sample_metadata.loc[common_samples] + self.df_labels = self.df_labels.loc[common_samples] + self.df_rnaseq = self.df_rnaseq.loc[common_samples] + + if self.min_n_label > 0: + # Keep only labels with at least min_n_label non-missing values + self.df_labels.dropna(axis=1, thresh=self.min_n_label, inplace=True) + # Warning if df_labels is empty + if len(self.df_labels.columns) == 0: + logger.warning( + f"df_labels is empty after dropping labels with less than {self.min_n_label} non-missing values." + ) + + def _filter_tissues(self) -> None: + """Filter samples based on tissues_to_keep and tissues_to_exclude.""" + + def rename_for_code(x: str) -> str: + """Normalize tissue names.""" + return str(x).lower().replace(" ", "_").replace("-", "_") + + # Keep only samples with a tissue in tissues_to_keep + if self.tissues_to_keep != "all": + self.df_sample_metadata = self.df_sample_metadata.dropna(subset="tissue") + self.df_sample_metadata = self.df_sample_metadata.loc[ + self.df_sample_metadata["tissue"] + .apply(rename_for_code) + .isin(pd.Series(self.tissues_to_keep).apply(rename_for_code)) + ] + + # Remove samples with a tissue in tissues_to_exclude + if self.tissues_to_exclude is not None: + self.df_sample_metadata = self.df_sample_metadata.dropna(subset="tissue") + self.df_sample_metadata = self.df_sample_metadata.loc[ + ~self.df_sample_metadata["tissue"] + .apply(rename_for_code) + .isin(pd.Series(self.tissues_to_exclude).apply(rename_for_code)) + ] + + def _sort_rows_and_columns(self) -> None: + """Sort rows and columns for consistency.""" + self.df_sample_metadata = self.df_sample_metadata.sort_index(axis=0) + self.df_rnaseq = self.df_rnaseq.sort_index(axis=0).sort_index(axis=1) + if self.label is not None: + self.df_labels = self.df_labels.sort_index(axis=0).sort_index(axis=1) + if self.df_fingerprints is not None: + self.df_fingerprints = self.df_fingerprints.sort_index(axis=0) + + def _format_dataframe(self) -> None: + """Format dataframes with proper data types.""" + self.df_rnaseq = self.df_rnaseq.astype("float64") + + def keep_perturbations(self, perturbation_names: list) -> None: + """Keep only the perturbations in the list in all dataframes. + + Parameters + ---------- + perturbation_names : list + The list of perturbations to keep. + """ + self.df_labels = self.df_labels[perturbation_names] + if self.df_fingerprints is not None: + self.df_fingerprints = self.df_fingerprints.loc[self.df_fingerprints.index.intersection(perturbation_names)] + if self.df_fingerprints.empty: + self.df_fingerprints = None + self._sort_rows_and_columns() + self.stack_dataframes() + + def merge(self, data: "PreclinicalDataset") -> None: + """Merge two PreclinicalDataset objects. + + Parameters + ---------- + data : PreclinicalDataset + The dataset to merge. + """ + # Concatenate labels and sample metadata + self.df_labels = pd.concat([self.df_labels, data.df_labels], axis=0) + self.df_sample_metadata = pd.concat([self.df_sample_metadata, data.df_sample_metadata], axis=0) + + # Update df_labels_stacked and df_sample_metadata_stacked + self._sort_rows_and_columns() + self.stack_dataframes() + + # Concatenate molecular data + if hasattr(data, "df_rnaseq"): + # Concatenate molecular data + common_columns = list(set(self.df_rnaseq.columns) & set(data.df_rnaseq.columns)) + # Keeps only common columns + self.df_rnaseq = self.df_rnaseq[common_columns] + data.df_rnaseq = data.df_rnaseq[common_columns] + self.df_rnaseq = pd.concat([self.df_rnaseq, data.df_rnaseq], axis=0) diff --git a/src/leap/data/preprocessor.py b/src/leap/data/preprocessor.py new file mode 100644 index 0000000..59362b7 --- /dev/null +++ b/src/leap/data/preprocessor.py @@ -0,0 +1,193 @@ +"""Preprocessing steps and basic feature selection for Omics data.""" + +from typing import Self + +import numpy as np +import pandas as pd +from loguru import logger +from sklearn.preprocessing import FunctionTransformer, MinMaxScaler, StandardScaler + + +SCALERS: dict[str, MinMaxScaler | StandardScaler | FunctionTransformer] = { + "min_max": MinMaxScaler(), + "mean_std": StandardScaler(with_std=True), + "mean": StandardScaler(with_std=False), + "identity": FunctionTransformer(func=None), +} + + +class OmicsPreprocessor: + """Preprocesses normalized Omics data. + + Transformations: + 1. Select genes in given gene list or protein-coding genes, if specified. + 2. Normalize data with provided scaler if pre_normalize == True. + 3. Compute ranks of genes according to gene_filtering method. + 4. Select top max_genes genes based on their ranks. + 3. If log_scaling == True, apply log(x+1). + 4. Data is centered with the chosen scaling method. + + Parameters + ---------- + scaling_method : str + Scaling method to apply after the log transformation (min_max, mean_std, mean), by default "min_max". + max_genes : int + Number of genes with highest variance to keep. Keep all genes if `max_genes <= 0`, by default -1. + log_scaling : bool + If True, apply a log transformation to the data (x: log(x+1)), by default True. + pre_normalize : bool + If True, normalizes the data before selecting genes with the gene_filtering method, by default False. + gene_list_source : str | list[str] | None + Path to CSV file containing a list of genes to be considered, or list of genes, by default None. + + Raises + ------ + ValueError + If the scaler method is unknown. + """ + + def __init__( + self, + scaling_method: str = "min_max", + max_genes: int = -1, + log_scaling: bool = True, + pre_normalize: bool = False, + gene_list_source: str | list[str] | None = None, + ): + self.scaling_method = scaling_method + self.max_genes = max_genes + self.gene_list: list[str] | None = None + if gene_list_source: + if isinstance(gene_list_source, str): + # Path to file containing the list of genes + self.gene_list = pd.read_csv(gene_list_source, header=None).iloc[:, 0].tolist() + else: + self.gene_list = gene_list_source + + if self.scaling_method not in SCALERS: + raise ValueError(f"Scaling method must be {SCALERS.keys()}, got '{self.scaling_method}'") + + self.scaler = SCALERS[self.scaling_method] + self.log_scaling = log_scaling + self.columns_to_keep: list[str] = [] + self.pre_normalize = pre_normalize + + def fit(self, X: pd.DataFrame) -> Self: + """Compute gene list and fit the scaler used for later transformation. + + Parameters + ---------- + X : pd.DataFrame + Omics data (untransformed). + + Returns + ------- + Self + self + """ + # If the processor has already been used in pretraining or DA for instance skip. + if not self.columns_to_keep: + # Do gene filtering on pre specific gene list. + if self.gene_list is not None: + X = X[X.columns.intersection(self.gene_list)] + + # Do specific filtering like variance or wasserstein. + if self.max_genes > 0: + gene_ranks = self.rank_genes(X) + logger.info( + f"Selecting {self.max_genes} genes based on" + f" variance filtering" + (", pre-normalized" if self.pre_normalize else "") + "." + ) + # Save columns to keep for future fit_transform. + # The sort values is going to "revert" the second argsort in the + # rank_genes function. You will have the index of the highest variant + # columns: [index_of_most_variant_genes ,.._second_most_variant, etc] + # Sorting the columns to ensure the order is consistent. + self.columns_to_keep = sorted(gene_ranks.sort_values()[: self.max_genes].index.tolist()) + else: + # No gene filtering so you take all columns of X. + # Save columns to keep for future fit_transform. + # Sorting the columns to ensure the order is consistent. + self.columns_to_keep = sorted(X.columns.tolist()) + + if not set(self.columns_to_keep) <= set(X.columns): + logger.warning("X does not have all the columns to keep") + self.columns_to_keep = X.columns.intersection(self.columns_to_keep).tolist() + X = X[self.columns_to_keep] + # log transform + if self.log_scaling is True: + X = X.apply(np.log1p) + + # train scaler + self.scaler.fit(X) + + return self + + def rank_genes(self, X: pd.DataFrame) -> pd.Series: + """Rank genes. + + Rank genes according to each method contained in self.gene_filtering, then take the minimum rank of each gene + across methods. + + Parameters + ---------- + X : pd.DataFrame + Omics data (Filtered on gene list, possibly log-scaled). + + Returns + ------- + pd.Series + gene rank. + """ + gene_ranks = {} + variances = np.var(X, axis=0) + # Ranks, highest variance first + # sorts the elements of the "variances" array in ascending order and + # returns the indices that would sort the array. Then redo an argsort + # to have the indices of the largest variances ranked. + ranks = (-variances).argsort().argsort() + # [rank_of_gene1,rank_of_gene2] + # This is needed to combine multiple filtering together. + # Add ranks to results dict + gene_ranks["variance"] = ranks + # Perform union of methods : take min ranks across methods + min_gene_ranks: pd.Series = pd.concat([ranks for _, ranks in gene_ranks.items()], axis=1).apply(min, axis=1) + + return min_gene_ranks + + def transform(self, X: pd.DataFrame) -> pd.DataFrame: + """Perform gene selection and data scaling with the chosen method. + + Parameters + ---------- + X : pd.DataFrame + Omics data (untransformed). + + Returns + ------- + pd.DataFrame + Transformed Omics data after gene selection and scaling. + """ + X = X[self.columns_to_keep].copy() + if self.log_scaling: + X = X.apply(np.log1p) + + # Keep X a pd DataFrame after scaling, not a np ndarray + X = pd.DataFrame(self.scaler.transform(X.astype(float)), columns=X.columns, index=X.index) + return X + + def fit_transform(self, X: pd.DataFrame) -> pd.DataFrame: + """Compute gene list to be kept, fit scaler, and apply data transformation. + + Parameters + ---------- + X : pd.DataFrame + Omics data (untransformed). + + Returns + ------- + pd.DataFrame + Transformed data after gene selection and scaling. + """ + self.fit(X) + return self.transform(X) diff --git a/src/leap/data/splits.py b/src/leap/data/splits.py new file mode 100644 index 0000000..31270b9 --- /dev/null +++ b/src/leap/data/splits.py @@ -0,0 +1,608 @@ +"""Implementation of the splits for the perturbation model.""" + +import random +from collections.abc import Generator +from functools import partial +from pathlib import Path +from typing import Any + +import numpy as np +import pandas as pd +from sklearn.model_selection import ( + GroupKFold, + GroupShuffleSplit, + KFold, + ShuffleSplit, + StratifiedKFold, + StratifiedShuffleSplit, +) + + +def _validate_cv_split_params( # noqa: PLR0912 + k_fold: bool, + group_variable: str | None, + subgroup_variable: str | None, + stratify_variable: str | None, + leave_one_group_out: bool, + test_split_ratio: float | None, + training_split_count: int | None, + n_splits: int | None, +) -> None: + """Validate cross-validation split parameters. + + Parameters + ---------- + k_fold : bool + Whether to use K-fold splits. + group_variable : str | None + Column name for grouping. + subgroup_variable : str | None + Column name for subgrouping. + stratify_variable : str | None + Column name for stratification. + leave_one_group_out : bool + Whether to use leave-one-group-out. + test_split_ratio : float | None + Ratio of test set size. + training_split_count : int | None + Number of training samples. + n_splits : int | None + Number of splits to generate. + + Raises + ------ + ValueError + If parameter combinations are invalid. + NotImplementedError + If feature combinations are not yet supported. + """ + # Validate leave_one_group_out compatibility + if leave_one_group_out: + if group_variable is None: + raise ValueError("Cannot leave one group out without a grouping variable.") + if stratify_variable is not None: + raise NotImplementedError("Cannot stratify and leave one group out at the same time.") + if subgroup_variable is not None: + raise NotImplementedError("Cannot split by subgroup and leave one group out at the same time.") + if n_splits is not None: + raise ValueError("Cannot fix the number of splits with leave one group out.") + if test_split_ratio is not None: + raise ValueError("Cannot fix the test split ratio with leave one group out.") + if training_split_count is not None: + raise ValueError("Cannot fix the number of training samples with leave one group out.") + if k_fold: + raise ValueError("Cannot do K fold and leave one group out at the same time.") + + # Validate k_fold compatibility + if k_fold: + if test_split_ratio is not None: + raise ValueError("Cannot fix the test split ratio with K folds.") + + # Validate test_split_ratio + if test_split_ratio is not None: + if test_split_ratio <= 0: + raise ValueError("Test split ratio must be > 0.") + if test_split_ratio >= 1: + raise ValueError("Test split ratio must be < 1.") + + # Validate training_split_count compatibility + if training_split_count is not None: + if test_split_ratio is not None: + raise ValueError("Either test_split_ratio or training_split_count should be provided, not both.") + if k_fold: + raise ValueError("Cannot use k_fold if training_split_count is provided.") + if leave_one_group_out: + raise ValueError("Cannot use leave_one_group_out if training_split_count is provided.") + + # Validate subgroup_variable compatibility + if subgroup_variable is not None: + if training_split_count is None: + raise ValueError("training_split_count must be provided when using subgroup_variable.") + + # Validate group + stratify combination + if test_split_ratio is not None and group_variable is not None and stratify_variable is not None: + raise NotImplementedError("Cannot stratify and group at the same time.") + + +def cv_split_ids( + X_metadata: pd.DataFrame, + k_fold: bool = False, + group_variable: str | None = None, + subgroup_variable: str | None = None, + stratify_variable: str | None = None, + leave_one_group_out: bool = False, + test_split_ratio: float = 0.2, + training_split_count: int | None = None, + n_splits: int = 10, + n_min_loo: int = 15, + list_test_groups: Path | None = None, + random_state: int = 0, +) -> dict[str, dict[str, list]]: + """Training and test indices of the dataset for cross-validation. + + This function supports shuffle, group shuffle, stratified shuffle splits, K fold or group K fold. It can also be + used for leave-one-group-out cross validation. + + Parameters + ---------- + X_metadata : pd.DataFrame + Input data. + k_fold : bool + Whether to split the data into K folds, by default False. If False, the data is split into non-overlapping + training and test sets. + group_variable : str | None + Column name in X_metadata to use for groups, by default None. + subgroup_variable : str | None + Column name in X_metadata to split data into subgroups for selecting a fixed number of training samples + (training_split_count) per subgroup, by default None. + stratify_variable : str | None + Column name in X_metadata to use to stratify, by default None. + leave_one_group_out : bool + Whether to perform leave-one-group-out, by default False. If True, group_variable must be provided. + test_split_ratio : float + Ratio of the test set size over the total sample size in df_all, by default 0.2. This should be between 0 and 1 + or should be set to None for leave-one-group-out. + training_split_count : int | None + Number of samples in the test set. Only used if test_split_ratio is None. + n_splits : int + Number of splits generate, by default 10. This should be set to None for leave-one-group-out. This corresponds + to the number of folds if k_fold is True. + n_min_loo : int + Minimum number of samples to keep a test set in leave-one-group-out, by default 15. We only keep splits that + have at least n_min_loo samples for all perturbations. + list_test_groups : Path | None + Path to a csv file listing the groups to use as test sets. Only used if leave_one_group_out is True. + random_state : int + Random seed used by the split generator, by default 0. + + Raises + ------ + ValueError + If leave_one_group_out is True and group_variable is None. + If leave_one_group_out is True and stratify_variable is not None. + If leave_one_group_out is True and n_splits is not None. + If leave_one_group_out is True and test_split_ratio is not None. + If test_split_ratio is 0 and n_splits is not 1. + If test_split_ratio is 1 and n_splits is not 1. + + Returns + ------- + dict[str, dict[str, list]] + Dictionary with training and test set ids. + """ + # Initialise the dictionary to store the split ids + split_ids: dict[str, dict[str, list]] = {} + + # Store the split ids + if test_split_ratio == 0: + if n_splits != 1: + raise ValueError("Cannot have more than one split with test_split_ratio = 0.") + + # Use all samples for training + split_ids["split_0"] = {"training_ids": X_metadata.index.to_list(), "test_ids": []} + elif test_split_ratio == 1: + if n_splits != 1: + raise ValueError("Cannot have more than one split with test_split_ratio = 1.") + + # Use all samples for test + split_ids["split_0"] = {"training_ids": [], "test_ids": X_metadata.index.to_list()} + else: + # Create the generator + split_generator = cv_split_generator( + X_metadata=X_metadata, + k_fold=k_fold, + group_variable=group_variable, + subgroup_variable=subgroup_variable, + stratify_variable=stratify_variable, + leave_one_group_out=leave_one_group_out, + test_split_ratio=test_split_ratio, + training_split_count=training_split_count, + n_splits=n_splits, + n_min_loo=n_min_loo, + list_test_groups=list_test_groups, + return_group=True, + random_state=random_state, + ) + + # Extract the training and test ids from the generator + if leave_one_group_out: + for training_ids, test_ids, loo_group in split_generator: + split_ids[f"split_{loo_group}"] = { + "training_ids": X_metadata.index[training_ids].to_list(), + "test_ids": X_metadata.index[test_ids].to_list(), + # Storing information on the group used as test set + "ood_test": loo_group, + } + else: + for split in range(n_splits): + training_ids, test_ids = next(split_generator) + split_ids[f"split_{split}"] = { + "training_ids": X_metadata.index[training_ids].to_list(), + "test_ids": X_metadata.index[test_ids].to_list(), + } + + return split_ids + + +def cv_split_generator( + X_metadata: pd.DataFrame, + k_fold: bool = False, + group_variable: str | None = None, + subgroup_variable: str | None = None, + stratify_variable: str | None = None, + leave_one_group_out: bool = False, + test_split_ratio: float | None = 0.2, + training_split_count: int | None = None, + n_splits: int | None = 10, + n_min_loo: int = 15, + list_test_groups: Path | None = None, + return_group: bool = False, + random_state: int = 0, +) -> Generator: + """Create generator for cross validation. + + This function supports shuffle, group shuffle, stratified shuffle splits, K fold or group K fold. It can also be + used for leave-one-group-out cross validation. + + Parameters + ---------- + X_metadata : pd.DataFrame + Input data. + k_fold : bool + Whether to split the data into K folds, by default False. If False, the data is split into non-overlapping + training and test sets. + group_variable : str | None + Column name in X_metadata to use for groups, by default None. + subgroup_variable : str | None + Column name in X_metadata to split data into subgroups for selecting a fixed number of training samples + (training_split_count) per subgroup, by default None. + stratify_variable : str | None + Column name in X_metadata to use to stratify, by default None. + leave_one_group_out : bool + Whether to perform leave-one-group-out, by default False. If True, group_variable must be provided. + test_split_ratio : float | None + Ratio of the test set size over the total sample size in df_all, by default 0.2. This should be strictly between + 0 and 1 or should be set to None for leave-one-group-out. + training_split_count : int | None + Number of samples in the test set. Only used if test_split_ratio is None. + n_splits : int | None + Number of splits generate, by default 10. This should be set to None for leave-one-group-out. This corresponds + to the number of folds if k_fold is True. + n_min_loo : int + Minimum number of samples to keep a test set in leave-one-group-out, by default 15. We only keep splits that + have at least n_min_loo samples for all perturbations. + list_test_groups : Path | None + Path to a csv file listing the groups to use as test sets. Only used if leave_one_group_out is True. + return_group : bool + Whether to return the group value, by default False. + random_state : int + Random seed used by the split generator, by default 0. + + Returns + ------- + Generator + Generator for training and test set indexes. + """ + # Validate all parameters + _validate_cv_split_params( + k_fold=k_fold, + group_variable=group_variable, + subgroup_variable=subgroup_variable, + stratify_variable=stratify_variable, + leave_one_group_out=leave_one_group_out, + test_split_ratio=test_split_ratio, + training_split_count=training_split_count, + n_splits=n_splits, + ) + + # Generate training and test splits + if test_split_ratio is not None: + # Create data-specific split generator + if group_variable is not None: + # Use group shuffle split + split_iterator = GroupShuffleSplit(n_splits=n_splits, test_size=test_split_ratio, random_state=random_state) + split_generator = split_iterator.split(X=np.arange(len(X_metadata)), groups=X_metadata[group_variable]) + elif stratify_variable is not None: + # Use stratified shuffle split + split_iterator = StratifiedShuffleSplit( + n_splits=n_splits, test_size=test_split_ratio, random_state=random_state + ) + split_generator = split_iterator.split(X=np.arange(len(X_metadata)), y=X_metadata[stratify_variable]) + else: + # Use shuffle split + split_iterator = ShuffleSplit(n_splits=n_splits, test_size=test_split_ratio, random_state=random_state) + split_generator = split_iterator.split(X=np.arange(len(X_metadata))) + elif subgroup_variable is not None and training_split_count is not None and n_splits is not None: + split_generator = _leave_n_samples_out_split_generator( + X_metadata=X_metadata, + subgroup_variable=subgroup_variable, + training_split_count=training_split_count, + n_splits=n_splits, + stratify_variable=stratify_variable, + random_state=random_state, + ) + elif leave_one_group_out and group_variable is not None: + # Use leave one group out + split_generator = leave_one_group_out_split_generator( + df_all=X_metadata, + group_variable=group_variable, + n_min_loo_variable="perturbation", + n_min_loo=n_min_loo, + list_test_groups=list_test_groups, + return_group=return_group, + ) + elif k_fold: + if group_variable is not None: + # Use group K fold + group_k_fold_iterator = GroupKFold(n_splits=n_splits) + split_generator = group_k_fold_iterator.split( + X=np.arange(len(X_metadata)), groups=X_metadata[group_variable] + ) + elif stratify_variable is not None: + # Use stratified K fold + stratified_k_fold_iterator = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=random_state) + split_generator = stratified_k_fold_iterator.split( + X=np.arange(len(X_metadata)), y=X_metadata[stratify_variable] + ) + else: + # Use K fold + k_fold_iterator = KFold(n_splits=n_splits, shuffle=True, random_state=random_state) + split_generator = k_fold_iterator.split(X=np.arange(len(X_metadata))) + + return split_generator + + +def _leave_one_group_out_split_generator( + df_all: pd.DataFrame, + group_variable: str, + n_min_loo: int = 15, + n_min_loo_variable: str | None = None, + list_test_groups: Path | None = None, +) -> Generator[tuple[list, list, str]]: + """Leave-one-group-out split generator. + + This function generates training and test splits for leave-one-group-out cross-validation. + + Parameters + ---------- + df_all : pd.DataFrame + Input dataset to split in training/test sets. + group_variable : str + Column name to use for groups. + n_min_loo : int + Minimum number of samples to keep a test set in leave-one-group-out, by default 15. + n_min_loo_variable : str | None + Column name to use to stratify when checking the number of available samples, by default None. + list_test_groups : Path | None + Path to a csv file listing the groups to use as test sets. + + Yields + ------ + Generator[tuple[list, list, str]] + Training and test set indexes and the group value. + + Returns + ------- + Generator[tuple[list, list, str]] + Training and test set indexes and the group value. + """ + # Define the list of groups that can be used as test sets + unique_values_of_ood = df_all[group_variable].unique().tolist() + if list_test_groups is not None: + # Only the groups listed in the csv file can be used as test sets + test_groups = pd.read_csv(list_test_groups, header=None)[0] + unique_values_of_ood = list(set(unique_values_of_ood).intersection(test_groups)) + for value in unique_values_of_ood: + # Check the number of available samples + if n_min_loo_variable is not None: + # Check the minimum number of samples by perturbation for this tissue + # We want to make sure that we have at least n_min samples per perturbation + # and per tissue. Otherwise, the tissue is not used as a test set. + n_min = df_all[[group_variable, n_min_loo_variable]].reset_index(drop=True).value_counts()[value].min() + else: + # Check the number of available samples for this tissue + n_min = df_all[group_variable].value_counts()[value] + + # Check if there are enough samples in the tissue + if n_min >= n_min_loo: + # Create the generator with training ids, test ids and the tissue name + df_all = df_all.reset_index(drop=True) + test = df_all[df_all[group_variable] == value] + train = df_all[df_all[group_variable] != value] + yield train.index.tolist(), test.index.tolist(), value + + +def _leave_on_group_out_split_generator_no_group( + df_all: pd.DataFrame, + group_variable: str, + n_min_loo: int = 15, + n_min_loo_variable: str | None = None, + list_test_groups: Path | None = None, +) -> Generator[tuple[list, list]]: + """Leave-one-group-out split generator without group output. + + This function generates training and test splits for leave-one-group-out cross-validation. + + Parameters + ---------- + df_all : pd.DataFrame + Input dataset to split in training/test sets. + group_variable : str + Column name to use for groups. + n_min_loo : int + Minimum number of samples to keep a test set in leave-one-group-out, by default 15. + n_min_loo_variable : str | None + Column name to use to stratify when checking the number of available samples, by default None. + list_test_groups : Path | None + Path to a csv file listing the groups to use as test sets. + + Yields + ------ + Generator[tuple[list, list]] + Training and test set indexes. + + Returns + ------- + Generator[tuple[list, list]] + Training and test set indexes. + """ + generator = _leave_one_group_out_split_generator( + df_all=df_all, + group_variable=group_variable, + n_min_loo=n_min_loo, + n_min_loo_variable=n_min_loo_variable, + list_test_groups=list_test_groups, + ) + for training_ids, test_ids, _ in generator: + yield training_ids, test_ids + + +def leave_one_group_out_split_generator( + df_all: pd.DataFrame, + group_variable: str, + n_min_loo: int = 15, + n_min_loo_variable: str | None = None, + list_test_groups: Path | None = None, + return_group: bool = False, +) -> Generator[tuple[list, list, str]] | Generator[tuple[list, list]]: + """Leave-one-group-out split generator. + + This function generates training and test splits for leave-one-group-out cross + validation. + + Parameters + ---------- + df_all : pd.DataFrame + Input dataset to split in training/test sets. + group_variable : str + Column name to use for groups. + n_min_loo : int + Minimum number of samples to keep a test set in leave-one-group-out, by default 15. + n_min_loo_variable : str | None + Column name to use to stratify when checking the number of available samples, by default None. + list_test_groups : Path | None + Path to a csv file listing the groups to use as test sets. + return_group : bool + Whether to return the group value, by default False. + + Returns + ------- + Generator[tuple[list, list, str]] | Generator[tuple[list, list]] + Training and test set indexes and the group value. + """ + if return_group: + return _leave_one_group_out_split_generator( + df_all=df_all, + group_variable=group_variable, + n_min_loo=n_min_loo, + n_min_loo_variable=n_min_loo_variable, + list_test_groups=list_test_groups, + ) + else: + return _leave_on_group_out_split_generator_no_group( + df_all=df_all, + group_variable=group_variable, + n_min_loo=n_min_loo, + n_min_loo_variable=n_min_loo_variable, + list_test_groups=list_test_groups, + ) + + +def _leave_n_samples_out_split_generator( + X_metadata: pd.DataFrame, + subgroup_variable: str, + training_split_count: int, + n_splits: int, + stratify_variable: str | None = None, + random_state: int = 0, +) -> Generator[tuple[list, list]]: + """Leave n samples out split generator. + + Parameters + ---------- + X_metadata : pd.DataFrame + Input data. + subgroup_variable : str + Column name in X_metadata to split data into subgroups for selecting a fixed number of training samples + (training_split_count) per subgroup. + training_split_count : int + Number of samples in the test set. + n_splits : int + Number of splits generate, by default 10. This should be set to None for leave-one-group-out. This corresponds + to the number of folds if k_fold is True. + stratify_variable : str | None + Column name in X_metadata to use to stratify, by default None. + random_state : int + Random seed used by the split generator, by default 0. + + Yields + ------ + Generator[tuple[list, list]] + Training and test set indexes. + """ + # Set the seed + random.seed(random_state) + + # Iterate over the number of splits + for split in range(n_splits): + # Get training and test set ids per stratum + split_per_stratum = ( + X_metadata.reset_index(drop=True) + .groupby(subgroup_variable) + .apply( + partial( + _group_shuffle_split_by_count, + training_split_count=training_split_count, + stratify_variable=stratify_variable, + random_state=split, + ), + include_groups=False, + ) + ) + + # Combining the splits per stratum into a single split + training_ids: list[Any] = [] + test_ids: list[Any] = [] + for perturb_dict in split_per_stratum: + training_ids = training_ids + perturb_dict["training_ids"] + test_ids = test_ids + perturb_dict["test_ids"] + + yield training_ids, test_ids + + +def _group_shuffle_split_by_count( + df_by_stratum: pd.DataFrame, + training_split_count: int, + stratify_variable: str | None = None, + random_state: int | None = None, +) -> dict[str, list]: + """Select training samples by count with optional stratification. + + Parameters + ---------- + df_by_stratum : pd.DataFrame + Data for a single stratum. + training_split_count : int + Number of samples to select for training. + stratify_variable : str | None + Column name to use for stratification, by default None. + random_state : int | None + Random seed, by default None. + + Returns + ------- + dict[str, list] + Dictionary with 'training_ids' and 'test_ids' lists. + """ + if stratify_variable is None: + training_ids = random.sample(list(df_by_stratum.index), k=training_split_count) + else: + split_iterator = StratifiedShuffleSplit(n_splits=1, test_size=training_split_count, random_state=random_state) + + # Generate the stratified split + train_idx, _ = next(split_iterator.split(df_by_stratum, df_by_stratum[stratify_variable])) + training_ids = df_by_stratum.index[train_idx].tolist() + + test_ids = list(set(df_by_stratum.index).difference(training_ids)) + + return {"training_ids": training_ids, "test_ids": test_ids} diff --git a/src/leap/metrics/__init__.py b/src/leap/metrics/__init__.py new file mode 100644 index 0000000..321626b --- /dev/null +++ b/src/leap/metrics/__init__.py @@ -0,0 +1 @@ +"""Metrics for LEAP.""" diff --git a/src/leap/metrics/regression_metrics.py b/src/leap/metrics/regression_metrics.py new file mode 100644 index 0000000..9c91760 --- /dev/null +++ b/src/leap/metrics/regression_metrics.py @@ -0,0 +1,96 @@ +"""Regression metrics.""" + +import warnings +from typing import Literal, TypeAlias + +import numpy as np +import pandas as pd +from scipy.stats import pearsonr, spearmanr +from sklearn.metrics import mean_absolute_error as mae +from sklearn.metrics import mean_squared_error as mse +from sklearn.metrics import r2_score + + +REGRESSION_METRICS = ("spearman", "pearson", "r2", "mse", "mae") +RegressionMetricType: TypeAlias = Literal["spearman", "pearson", "r2", "mse", "mae"] + + +def performance_metric_wrapper( + y_true: pd.Series, y_pred: pd.Series, metric: RegressionMetricType = "spearman", per_perturbation: bool = False +) -> float: + """Calculate a performance metric. + + This function accommodates missing values in the true labels: these are excluded before calculating the performances + + Parameters + ---------- + y_true : pd.Series + True labels. + y_pred : pd.Series + Predicted labels. + metric : RegressionMetricType, optional + Metric to compute, by default "spearman". Possible values are: "spearman", "pearson", "r2", "mse" and "mae". + per_perturbation : bool, optional + Whether to compute the performance metric per perturbation, by default False. + + Returns + ------- + float + Performance metric. + """ + # Exclude missing true labels + y_true = y_true.dropna() + y_pred = y_pred[y_true.index] + + # Compute the performance metric + if per_perturbation: + # Calculate the average per-perturbation metric + return ( + pd.DataFrame({"y_true": y_true, "y_pred": y_pred}) + .groupby("perturbation") + .apply(lambda x: performance_metric(x["y_true"].to_numpy(), x["y_pred"].to_numpy(), metric=metric)) + ).mean() + + # Calculate the overall metric + return performance_metric(y_true.to_numpy(), y_pred.to_numpy(), metric) + + +def performance_metric(y_true: np.ndarray, y_pred: np.ndarray, metric: RegressionMetricType = "spearman") -> float: + """Calculate a performance metric for a continuous label. Correlation metrics for constant outputs are set to 0. + + Parameters + ---------- + y_true : np.ndarray + True labels. + y_pred : np.ndarray + Predicted labels. + metric : RegressionMetricType, optional + Metric to compute, by default "spearman". Possible values are: "spearman", "pearson", "r2", "mse" and "mae". + + Returns + ------- + float + Performance metric. + + Raises + ------ + ValueError + If the metric is not implemented. + """ + if metric == "r2": + return r2_score(y_true, y_pred) + if metric == "mse": + return mse(y_true, y_pred) + if metric == "mae": + return mae(y_true, y_pred) + # For correlation metrics, we set the metric to 0 if the outputs are constant + if metric == "spearman": + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + return np.nan_to_num(spearmanr(y_true, y_pred, nan_policy="omit")[0]) + if metric == "pearson": + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + return np.nan_to_num(pearsonr(y_true, y_pred)[0]) + + raise ValueError(f"Unsupported metric: {metric}. Must be one of {REGRESSION_METRICS}") diff --git a/src/leap/pipelines/__init__.py b/src/leap/pipelines/__init__.py new file mode 100644 index 0000000..439d507 --- /dev/null +++ b/src/leap/pipelines/__init__.py @@ -0,0 +1 @@ +"""Pipelines for LEAP.""" diff --git a/src/leap/pipelines/perturbation_pipeline.py b/src/leap/pipelines/perturbation_pipeline.py new file mode 100644 index 0000000..899b417 --- /dev/null +++ b/src/leap/pipelines/perturbation_pipeline.py @@ -0,0 +1,1033 @@ +"""Perturbation pipeline for LEAP. Includes pre-processing, representation learning and regression.""" + +import copy +from collections.abc import Callable +from itertools import product +from pathlib import Path +from typing import Any, cast + +import numpy as np +import pandas as pd +import ray +from loguru import logger +from tqdm import tqdm + +from leap.data.preprocessor import OmicsPreprocessor +from leap.metrics.regression_metrics import RegressionMetricType, performance_metric_wrapper +from leap.regression_models import ElasticNet, KnnRegressor, RegressionModel +from leap.representation_models import RepresentationModelBase +from leap.utils.io import load_pickle, save_pickle + + +# Constants for magic strings +FOLD_PREFIX = "fold_" +FULL_TRAINING_KEY = "full_training_data" +SAMPLE_INDEX = "sample" +PERTURBATION_INDEX = "perturbation" + + +class PerturbationPipeline: + """Class for the perturbation regression model. + + Parameters + ---------- + preprocessor_model_rnaseq : OmicsPreprocessor | Path | None + Path to a trained preprocessor model or configuration of a preprocessor model to preprocess the input RNASeq + data. If None, no preprocessing is applied. + rpz_model_rnaseq : RepresentationModelBase | Path | None + Path to a trained RPZ model or configuration of a RPZ model to train the RPZ for RNASeq when running .fit(). + If None, no representation learning is applied. + regression_model_base_instance : RegressionModel + Configuration of the prediction model. + hpt_tuning_cv_split : Callable | None + Configuration to define the folds used in the cross validation for hyper-parameter tuning. The models trained in + each of these splits are ensembled if ensembling is True. + hpt_tuning_param_grid : dict | None + Configuration to define the grid of hyper-paramaters to try in the cross validation for hyper-parameter tuning. + hpt_tuning_score : RegressionMetricType | None + Score or scoring function to maximise for hyper-parameter tuning. Possible values are: "spearman", "pearson", + "r2", "mse", "mae". + fgpt_rpz_model : RepresentationModelBase | Path | None + Path to a trained RPZ model for fingerprints or a RPZ model for fingerprints to train the RPZ when running + .fit(). If None, no representation learning is applied to fingerprints. + one_model_per_perturbation : bool + Whether to train one model per perturbation, by default True. + ensembling : bool + Whether to average the predictions of the models trained in the different CV folds, by default True. Note that + ensembling cannot be set to True if hpt_tuning_cv_split is not provided. Default is True. + ensembling_save_models_to_disk : bool + Whether to save the ensembling models to disk, this can save RAM and prevent out of memory errors for heavy + models, by default False. + use_ray : bool + Whether to use Ray to parallelise over the perturbations. Only possible if one_model_per_perturbation is True. + Default is False. + ray_remote_params : dict | None + Parameters for Ray remote. Defaults to None. + + Raises + ------ + ValueError + If hpt_tuning_score is not provided when hpt_tuning_param_grid is provided. + """ + + def __init__( + self, + preprocessor_model_rnaseq: OmicsPreprocessor | Path | None, + rpz_model_rnaseq: RepresentationModelBase | Path | None, + regression_model_base_instance: RegressionModel, + hpt_tuning_cv_split: Callable | None, + hpt_tuning_param_grid: dict | None, + hpt_tuning_score: RegressionMetricType | None, + fgpt_rpz_model: RepresentationModelBase | Path | None, + one_model_per_perturbation: bool = True, + ensembling: bool = True, + ensembling_save_models_to_disk: bool = False, + use_ray: bool = False, + ray_remote_params: dict | None = None, + ): + # Store arguments + self.preprocessor_model_rnaseq = preprocessor_model_rnaseq + self.rpz_model_rnaseq = rpz_model_rnaseq + self.fgpt_rpz_model = fgpt_rpz_model + self.regression_model_base_instance = regression_model_base_instance + self.hpt_tuning_cv_split = hpt_tuning_cv_split + self.hpt_tuning_param_grid = hpt_tuning_param_grid + self.hpt_tuning_score = hpt_tuning_score + self.one_model_per_perturbation = one_model_per_perturbation + self.ensembling = ensembling + self.ensembling_save_models_to_disk = ensembling_save_models_to_disk + self.use_ray = use_ray + self.ray_remote_params = ray_remote_params or {"num_cpus": 1} + self.ensembling_output_path: Path | None = None + + # Check that arguments are compatible + if self.use_ray and not self.one_model_per_perturbation: + logger.warning("Ray is only available for one model per perturbation.") + self.use_ray = False + if self.hpt_tuning_cv_split is None: + if self.ensembling: + logger.warning("ensembling is set to False as hpt_tuning_cv_split is None.") + self.ensembling = False + if self.hpt_tuning_param_grid is not None: + logger.warning("hpt_tuning_param_grid is ignored as hpt_tuning_cv_split is None.") + elif self.hpt_tuning_param_grid is None: + self.hpt_tuning_param_grid = self.regression_model_base_instance.get_params() + self.hpt_tuning_param_grid = {k: [v] for k, v in self.hpt_tuning_param_grid.items()} + if self.hpt_tuning_cv_split and self.hpt_tuning_score is None: + raise ValueError("hpt_tuning_score must be provided if hpt_tuning_cv_split is provided.") + + # Initialise trained models (simplified from dict structure since only handling rnaseq) + self.trained_preprocessor: OmicsPreprocessor | None = None + self.trained_rpz_model: RepresentationModelBase | None = None + self.trained_fgpt_rpz_model: RepresentationModelBase | None = None + + # Nested dict for per-perturbation models, simple dict for pan-perturbation + self.trained_regression_model: dict[str, dict[str, RegressionModel]] | dict[str, RegressionModel] = {} + self.grid_search_regression_model: dict[str, pd.DataFrame] = {} + self.y_columns: pd.Index | None = None + + def fit( + self, + X: pd.DataFrame, + y: pd.DataFrame, + X_metadata: pd.DataFrame | None = None, + X_fgpt: pd.DataFrame | None = None, + ) -> None: + """Fit the perturbation prediction pipeline. + + This function fits a prediction model for each label in y. + There are four steps: + 1- Preprocess X using a (trained) preprocessor. + 2- Transform X using a (trained) RPZ model. + 3- Transform X_fgpt using a (trained) RPZ model. Optional, this is only done if X_fgpt is provided. + 4- Train a prediction model for each label in y. + + Parameters + ---------- + X : pd.DataFrame + Input data. + y : pd.DataFrame + Labels. + X_metadata : pd.DataFrame | None + Metadata including the grouping variable for preprocessor and/or the grouping variable for cross-validation + splits as columns. Default is None. + X_fgpt : pd.DataFrame | None + Fingerprints. Default is None. + + Raises + ------ + ValueError + If fingerprint data is not provided when fitting and one_model_per_perturbation is False. + """ + # Ensure metadata exists + X_metadata = self._ensure_metadata(X, X_metadata) + + # Preprocess the input data + X = self._preprocessor_transform(X, allow_fit=True) + + # Transform input data using rpz model + X = self._rpz_transform(X, allow_fit=True) + + # Fit regression models + if self.one_model_per_perturbation: + # Fit one model per perturbation (fingerprint data is not used) + self._fit_per_perturbation(X=X, y=y, X_metadata=X_metadata) + else: + if X_fgpt is None: + raise ValueError( + "Fingerprint data must be provided when fitting and one_model_per_perturbation is False." + ) + # Transform fingerprint data using fingerprint rpz model + X_fgpt = self._rpz_fgpt_transform(X_fgpt, allow_fit=True) + # Fit one model for all perturbations + self._fit_all_perturbations(X=X, y=y, X_fgpt=X_fgpt, X_metadata=X_metadata) + + def _ensure_metadata(self, X: pd.DataFrame, X_metadata: pd.DataFrame | None) -> pd.DataFrame: + """Create empty metadata dataframe if None.""" + if X_metadata is None: + return pd.DataFrame(index=X.index) + return X_metadata + + def _preprocessor_transform(self, X: pd.DataFrame, allow_fit: bool = True) -> pd.DataFrame: + """Transform the input data using the preprocessor model. + + If preprocessor_model is a preprocessor model and if allow_fit is True, this preprocessor model is trained on + the input data X and used to transform it. The trained preprocessor model is then stored as an attribute of the + class called trained_preprocessor. The argument allow_fit is True when calling this function in the fit method + on source domain data only, i.e. training is not permitted when using predict. + + If preprocessor_model is a path to a trained preprocessor model, this preprocessor model is directly used to + transform the input data X. It is also stored in trained_preprocessor. + + If preprocessor_model is None, the input data is not transformed. + + Parameters + ---------- + X : pd.DataFrame + Input data to preprocess. + allow_fit : bool + Whether to allow fitting the preprocessor. Default is True. + + Returns + ------- + pd.DataFrame + Preprocessed data. + + Raises + ------ + RuntimeError + If the preprocessor model is not fitted before transformation. + TypeError + If the preprocessor model is not a OmicsPreprocessor. + """ + if self.preprocessor_model_rnaseq is None: + return X + + if allow_fit: + if isinstance(self.preprocessor_model_rnaseq, Path): + # Load the trained preprocessor + self.trained_preprocessor = load_pickle(self.preprocessor_model_rnaseq) + else: + # Check the type of preprocessor + if not isinstance(self.preprocessor_model_rnaseq, OmicsPreprocessor): + raise TypeError("The preprocessor needs to be a OmicsPreprocessor.") + # Train the preprocessor on the data + self.preprocessor_model_rnaseq.fit(X) + self.trained_preprocessor = self.preprocessor_model_rnaseq + + if self.trained_preprocessor is None: + raise RuntimeError("Preprocessor must be fitted before transformation.") + + return self.trained_preprocessor.transform(X) + + def _rpz_transform(self, X: pd.DataFrame, allow_fit: bool = True) -> pd.DataFrame: + """Transform the input data using the RPZ model. + + If rpz_model is a RPZ model and if allow_fit is True, this model is trained on the input data X and used to + transform it. The trained RPZ model is then stored as an attribute of the class called trained_rpz_model. + + If rpz_model is a path to a trained RPZ model, this model is directly used to transform the input data X. + It is also stored in trained_rpz_model. + + If rpz_model is None, the input data is not transformed. + + Parameters + ---------- + X : pd.DataFrame + Input data to transform. + allow_fit : bool + Whether to allow fitting the RPZ model. Default is True. + + Returns + ------- + pd.DataFrame + Transformed data. + + Raises + ------ + RuntimeError + If the RPZ model is not fitted before transformation. + """ + if self.rpz_model_rnaseq is None: + return X + + if allow_fit: + if isinstance(self.rpz_model_rnaseq, Path): + # Load the trained RPZ model + self.trained_rpz_model = load_pickle(self.rpz_model_rnaseq) + else: + # Train the RPZ on the data + self.rpz_model_rnaseq.fit(X) + self.trained_rpz_model = self.rpz_model_rnaseq + + if self.trained_rpz_model is None: + raise RuntimeError("RPZ model must be fitted before transformation.") + + X_transformed = self.trained_rpz_model.transform(X) + return pd.DataFrame( + X_transformed, + index=X.index, + columns=[f"rnaseq_rpz_{i}" for i in range(self.trained_rpz_model.repr_dim)], + ) + + def _rpz_fgpt_transform(self, X_fgpt: pd.DataFrame, allow_fit: bool = True) -> pd.DataFrame: + """Transform fingerprint data using the fingerprint RPZ model. + + Parameters + ---------- + X_fgpt : pd.DataFrame + Fingerprint data to transform. + allow_fit : bool + Whether to allow fitting the model. Default is True. + + Returns + ------- + pd.DataFrame + Transformed fingerprint data. + + Raises + ------ + RuntimeError + If the fingerprint RPZ model is not fitted before transformation. + """ + if self.fgpt_rpz_model is None: + return X_fgpt + + if allow_fit: + if isinstance(self.fgpt_rpz_model, Path): + # Load the trained fingerprint RPZ model + self.trained_fgpt_rpz_model = load_pickle(self.fgpt_rpz_model) + else: + # Train the fingerprint RPZ on the data + self.fgpt_rpz_model.fit(X_fgpt) + self.trained_fgpt_rpz_model = self.fgpt_rpz_model + + if self.trained_fgpt_rpz_model is None: + raise RuntimeError("Fingerprint RPZ model must be fitted before transformation.") + + X_fgpt_transformed = self.trained_fgpt_rpz_model.transform(X_fgpt) + return pd.DataFrame( + X_fgpt_transformed, + index=X_fgpt.index, + columns=[f"fgpt_rpz_{i}" for i in range(self.trained_fgpt_rpz_model.repr_dim)], + ) + + def _fit_per_perturbation( + self, + X: pd.DataFrame, + y: pd.DataFrame, + X_metadata: pd.DataFrame, + ) -> None: + """Fit one model per perturbation.""" + # Define index names + X.index.name = SAMPLE_INDEX + y.index.name = SAMPLE_INDEX + X_metadata.index.name = SAMPLE_INDEX + + # Initialize as nested dict for per-perturbation models + self.trained_regression_model = {} + + # Fit models for each perturbation + if self.use_ray: + dict_grid_per_perturbation = self._fit_with_ray(X, y, X_metadata) + else: + dict_grid_per_perturbation = self._fit_sequential(X, y, X_metadata) + + # Store the outputs in attributes + self._store_per_perturbation_results(dict_grid_per_perturbation) + + def _fit_with_ray(self, X: pd.DataFrame, y: pd.DataFrame, X_metadata: pd.DataFrame) -> dict: + """Fit models using Ray for parallelization.""" + + @ray.remote(**self.ray_remote_params) + def _fit_one_perturbation_with_ray(*args: Any, **kwargs: Any) -> dict[str, Any]: + """Wrap _fit_one_perturbation to flag it with ray.remote().""" + return _fit_one_perturbation(*args, **kwargs) + + ray.init(ignore_reinit_error=True) + futures = { + label: _fit_one_perturbation_with_ray.remote( + x_data=X, + y_data=y[label], + X_metadata=X_metadata, + regression_model_base_instance=self.regression_model_base_instance, + hpt_tuning_param_grid=self.hpt_tuning_param_grid, + hpt_tuning_cv_split=self.hpt_tuning_cv_split, + hpt_tuning_score=self.hpt_tuning_score, + ensembling=self.ensembling, + ensembling_save_models_to_disk=self.ensembling_save_models_to_disk, + ensembling_output_path=self.ensembling_output_path, + pbar=None, + ) + for label in y.columns + } + return {key: ray.get(value) for key, value in futures.items()} + + def _fit_sequential(self, X: pd.DataFrame, y: pd.DataFrame, X_metadata: pd.DataFrame) -> dict: + """Fit models sequentially with progress bar.""" + pbar = tqdm(y.columns, desc="Perturbation", leave=True) + return { + label: _fit_one_perturbation( + x_data=X, + y_data=y[label], + X_metadata=X_metadata, + regression_model_base_instance=self.regression_model_base_instance, + hpt_tuning_param_grid=self.hpt_tuning_param_grid, + hpt_tuning_cv_split=self.hpt_tuning_cv_split, + hpt_tuning_score=self.hpt_tuning_score, + ensembling=self.ensembling, + ensembling_save_models_to_disk=self.ensembling_save_models_to_disk, + ensembling_output_path=self.ensembling_output_path, + pbar=pbar, + ) + for label in pbar + } + + def _store_per_perturbation_results(self, dict_grid_per_perturbation: dict) -> None: + """Store trained models and grid search results.""" + # Type narrow: we know this is nested dict for per-perturbation + trained_models = cast(dict[str, dict[str, RegressionModel]], self.trained_regression_model) + + for label, grid in dict_grid_per_perturbation.items(): + if self.ensembling: + for key, model in grid["best_ensemble_models_"].items(): + if key not in trained_models: + trained_models[key] = {} + trained_models[key][label] = model + + # Save trained regression model on all training data + if FULL_TRAINING_KEY not in trained_models: + trained_models[FULL_TRAINING_KEY] = {} + trained_models[FULL_TRAINING_KEY][label] = grid["best_estimator_"] + + # Save grid search results (None if no HPT) + self.grid_search_regression_model[label] = grid["cv_results_"] + + def _fit_all_perturbations( + self, + X: pd.DataFrame, + y: pd.DataFrame, + X_fgpt: pd.DataFrame, + X_metadata: pd.DataFrame, + ) -> None: + """Fit one model for all perturbations.""" + logger.info("Training a single regression model on all perturbations...") + + # Initialize as simple dict for pan-perturbation model + self.trained_regression_model = {} + + # Melt data if not using KNN (KNN handles multilabel directly) + if isinstance(self.regression_model_base_instance, KnnRegressor): + # Use common perturbations + common_perturbations = list(y.columns.intersection(X_fgpt.index)) + if len(common_perturbations) == 0: + raise ValueError("No common perturbations found between y and X_fgpt.") + if len(y.columns) != len(common_perturbations): + logger.warning(f"Perturbations not in fingerprints: {set(y.columns) - set(common_perturbations)}") + logger.warning(f"Perturbations not in labels: {set(X_fgpt.index) - set(common_perturbations)}") + y = y[common_perturbations] + X_fgpt = X_fgpt.loc[pd.Index(common_perturbations)] + X, X_metadata, y = self._melt_data(X, X_metadata, X_fgpt, y) + + # Fit the model on all perturbations + grid = _fit_single_label( + x_data=X, + y_data=y, + X_metadata=X_metadata, + regression_model_base_instance=self.regression_model_base_instance, + hpt_tuning_param_grid=self.hpt_tuning_param_grid, + hpt_tuning_cv_split=self.hpt_tuning_cv_split, + hpt_tuning_score=self.hpt_tuning_score, + ensembling=self.ensembling, + ensembling_save_models_to_disk=self.ensembling_save_models_to_disk, + ensembling_output_path=self.ensembling_output_path, + ) + + # Type narrow: we know this is simple dict for pan-perturbation + trained_models = cast(dict[str, RegressionModel], self.trained_regression_model) + + if self.ensembling: + for key, model in grid["best_ensemble_models_"].items(): + trained_models[key] = model + + # Save trained regression model on all training data + trained_models[FULL_TRAINING_KEY] = grid["best_estimator_"] + + # Save grid search results + self.grid_search_regression_model = grid["cv_results_"] + + # Store column names for prediction + self.y_columns = y.columns + + def _melt_data( + self, + X: pd.DataFrame, + X_metadata: pd.DataFrame, + X_fgpt: pd.DataFrame, + y: pd.DataFrame | None = None, + ) -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]: + """Melt the data for pan-perturbation models. + + Parameters + ---------- + X : pd.DataFrame + Input data. + X_metadata : pd.DataFrame + Metadata. + X_fgpt : pd.DataFrame + Fingerprints. + y : pd.DataFrame | None + Labels, by default None. + + Returns + ------- + tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame] + Melted input data, melted metadata and melted labels. + """ + if y is None: + # Create dummy label data + y = pd.DataFrame(index=X.index, columns=X_fgpt.index) + + # Melt the label data + y.index.name = SAMPLE_INDEX + y.columns.name = PERTURBATION_INDEX + y_concat = y.reset_index().melt(id_vars=SAMPLE_INDEX) + + # Align the input data + X_concat = y_concat.join(X, on=SAMPLE_INDEX).drop("value", axis=1) + X_fgpt.index.name = PERTURBATION_INDEX + X_fgpt_concat = y_concat.join(X_fgpt, on=PERTURBATION_INDEX).drop("value", axis=1) + X_concat = X_concat.set_index([PERTURBATION_INDEX, SAMPLE_INDEX]).sort_index() + X_fgpt_concat = X_fgpt_concat.set_index([PERTURBATION_INDEX, SAMPLE_INDEX]).sort_index() + y_concat = y_concat.set_index([PERTURBATION_INDEX, SAMPLE_INDEX]).sort_index() + X_full_concat = pd.concat([X_concat, X_fgpt_concat], axis=1) + + # Align the metadata with y index + X_metadata_concat = y_concat.merge(X_metadata, left_on=SAMPLE_INDEX, right_index=True)[X_metadata.columns] + X_metadata_concat[SAMPLE_INDEX] = X_metadata_concat.index.get_level_values(SAMPLE_INDEX) + X_metadata_concat[PERTURBATION_INDEX] = X_metadata_concat.index.get_level_values(PERTURBATION_INDEX) + + return X_full_concat, X_metadata_concat, y_concat + + def _load_model_from_path(self, model_path: Path) -> Any: + """Load a model from disk.""" + if model_path.suffix == ".pkl": + return load_pickle(model_path) + raise NotImplementedError(f"Loading model under type {model_path.suffix} is not implemented for ensembling") + + def _get_average_fold_prediction_for_ensembling( + self, X: pd.DataFrame, label_name: str | None = None, columns: list[str] | None = None + ) -> pd.Series | pd.DataFrame: + """Average predictions across all fold models for ensembling. + + Parameters + ---------- + X : pd.DataFrame + Input data for prediction. + label_name : str | None + Label name for per-perturbation models. None for pan-perturbation models. + columns : list[str] | None + Column names for DataFrame output. If None, returns Series. + + Returns + ------- + pd.Series | pd.DataFrame + Averaged predictions. + + Raises + ------ + TypeError + If model_or_dict is not a dict when label_name is provided. + """ + models_results = [] + for key, model_or_dict in self.trained_regression_model.items(): + if FOLD_PREFIX in key: + if label_name is not None: + # Per-perturbation case + if not isinstance(model_or_dict, dict): + raise TypeError("model_or_dict should be a dict when label_name is provided") + model_path = model_or_dict[label_name] + else: + # Pan-perturbation case - type narrow since label_name is None + if isinstance(model_or_dict, dict): + raise TypeError("model_or_dict should not be a dict for pan-perturbation models") + model_path = model_or_dict + + # Check if model_path is a path or a model + if isinstance(model_path, Path): + loaded_model = self._load_model_from_path(model_path) + else: + loaded_model = model_path + models_results.append(loaded_model.predict(X)) + + avg_predictions = np.mean(models_results, axis=0) + + if columns is None: + return pd.Series(avg_predictions, index=X.index) + return pd.DataFrame(avg_predictions, index=X.index, columns=columns) + + def predict( + self, + X: pd.DataFrame, + X_metadata: pd.DataFrame | None = None, + X_fgpt: pd.DataFrame | None = None, + list_of_perturbations: list | None = None, + preprocessor_transform: bool = True, + ) -> pd.DataFrame: + """Predict the labels for X. + + Parameters + ---------- + X : pd.DataFrame + Input data. + X_metadata : pd.DataFrame | None + Metadata including the grouping variable for preprocessor and/or the grouping variable for cross-validation + splits as columns. Default is None. + X_fgpt : pd.DataFrame | None + Fingerprints. + list_of_perturbations : list | None + List of perturbations to predict labels for, by default None. + preprocessor_transform : bool + Whether to transform the input data using the preprocessor, by default True. + + Returns + ------- + pd.DataFrame + Predicted labels. + """ + # Ensure metadata exists + X_metadata = self._ensure_metadata(X, X_metadata) + + # Preprocess the input data + if preprocessor_transform: + X = self._preprocessor_transform(X, allow_fit=False) + + # Transform input data using rpz models + X = self._rpz_transform(X, allow_fit=False) + + # Predict based on model type + if self.one_model_per_perturbation: + return self._predict_per_perturbation(X, X_fgpt, list_of_perturbations) + elif not isinstance(self.regression_model_base_instance, KnnRegressor): + return self._predict_single_model_with_fgpt(X, X_metadata, X_fgpt, list_of_perturbations) + else: + return self._predict_multilabel(X, list_of_perturbations) + + def _predict_per_perturbation( + self, X: pd.DataFrame, X_fgpt: pd.DataFrame | None, list_of_perturbations: list | None + ) -> pd.DataFrame: + """Predict using per-perturbation models.""" + # Get trained models + fold_id = FULL_TRAINING_KEY if FULL_TRAINING_KEY in self.trained_regression_model else f"{FOLD_PREFIX}0" + trained_regression_model_fold = self.trained_regression_model[fold_id] + if not isinstance(trained_regression_model_fold, dict): + raise TypeError("trained_regression_model_fold should be a dictionary for per-perturbation models.") + + seen_perturbations = list(trained_regression_model_fold.keys()) + + # Define the list of all perturbations to predict + if list_of_perturbations is None: + list_of_perturbations = X_fgpt.index.tolist() if X_fgpt is not None else seen_perturbations + + if len(list_of_perturbations) == 0: + raise ValueError("No perturbations to predict.") + + # Check for unseen perturbations + unseen_perturbations = set(list_of_perturbations) - set(seen_perturbations) + if unseen_perturbations: + raise NotImplementedError( + f"Predicting unseen perturbations is not supported. Unseen: {unseen_perturbations}" + ) + + # Predict for each perturbation + data = {} + for label_name in list_of_perturbations: + if self.ensembling: + # Average over models trained in the different CV folds + data[label_name] = self._get_average_fold_prediction_for_ensembling(X, label_name) + else: + models_full_training_data = self.trained_regression_model[FULL_TRAINING_KEY] + if not isinstance(models_full_training_data, dict): + raise TypeError("models_full_training_data should be a dict for per-perturbation models.") + model_label = models_full_training_data[label_name] + if model_label is None: + raise RuntimeError(f"Model for {label_name} must be trained before prediction.") + data[label_name] = pd.Series(model_label.predict(X), index=X.index) + + # Concatenate all predictions in a dataframe + return pd.concat(data, axis=1) + + def _predict_single_model_with_fgpt( + self, + X: pd.DataFrame, + X_metadata: pd.DataFrame, + X_fgpt: pd.DataFrame | None, + list_of_perturbations: list | None, + ) -> pd.DataFrame: + """Predict using single model with fingerprints.""" + if X_fgpt is None: + raise ValueError("Fingerprint data must be provided for a single model on all perturbations.") + + # Transform the fingerprint data using trained rpz + if self.trained_fgpt_rpz_model is None: + raise RuntimeError("Fingerprint RPZ model must be trained before prediction.") + + X_fgpt_transformed = pd.DataFrame( + self.trained_fgpt_rpz_model.transform(X_fgpt), + index=X_fgpt.index, + columns=[f"fgpt_rpz_{i}" for i in range(self.trained_fgpt_rpz_model.repr_dim)], + ) + + # Keep the perturbations that we want to predict only + if list_of_perturbations is not None: + if len(list_of_perturbations) == 0: + raise ValueError("list_of_perturbations is empty.") + X_fgpt_transformed = X_fgpt_transformed.loc[list_of_perturbations] + + # Melt the data + X_melted, _, _ = self._melt_data(X, X_metadata, X_fgpt_transformed) + + # Predict + if self.ensembling: + df_predicted = self._get_average_fold_prediction_for_ensembling(X_melted, columns=["predicted"]) + else: + model_full_data = self.trained_regression_model[FULL_TRAINING_KEY] + if isinstance(model_full_data, dict): + raise TypeError( + "trained_regression_model['full_training_data'] should not be a dictionary " + "for pan-perturbation models." + ) + if model_full_data is None: + raise RuntimeError("Model must be trained before prediction.") + df_predicted = pd.DataFrame(model_full_data.predict(X_melted), index=X_melted.index, columns=["predicted"]) + + # Pivot the dataframe + return df_predicted.reset_index().pivot(index=SAMPLE_INDEX, columns=PERTURBATION_INDEX, values="predicted") + + def _predict_multilabel(self, X: pd.DataFrame, list_of_perturbations: list | None) -> pd.DataFrame: + """Predict using multilabel regressor (no fingerprints).""" + # Type narrow: we know this is simple dict for pan-perturbation + trained_regression_model = cast(dict[str, RegressionModel], self.trained_regression_model) + + if self.y_columns is None: + raise RuntimeError("Labels must be provided for multilabel regressor.") + + if list_of_perturbations is not None: + unseen = [p for p in list_of_perturbations if p not in self.y_columns] + if unseen: + raise ValueError(f"The multilabel regressor cannot predict unseen perturbations: {unseen}") + + # Predict + if self.ensembling: + predictions = [model.predict(X) for model in trained_regression_model.values()] + df_predicted = pd.DataFrame(np.mean(predictions, axis=0), index=X.index, columns=self.y_columns) + else: + model_full_data = trained_regression_model[FULL_TRAINING_KEY] + df_predicted = pd.DataFrame(model_full_data.predict(X), index=X.index, columns=self.y_columns) + + return df_predicted + + +def _add_perturbation_index(df: pd.DataFrame | pd.Series, perturbation_name: str) -> pd.DataFrame | pd.Series: + """Add the perturbation name to a DataFrame in a multi index.""" + df.index = pd.MultiIndex.from_arrays( + [np.full(len(df), perturbation_name), df.index.get_level_values(SAMPLE_INDEX)], + names=[PERTURBATION_INDEX, SAMPLE_INDEX], + ) + return df + + +def _drop_missing_values_if_any( + x_data: pd.DataFrame, + y_data: pd.Series | pd.DataFrame, + X_metadata: pd.DataFrame, +) -> tuple[pd.DataFrame, pd.Series | pd.DataFrame, pd.DataFrame]: + """Drop rows with missing values in y_data (for single-label case).""" + if isinstance(y_data, pd.Series) or y_data.shape[1] == 1: + y_data = y_data.dropna() + x_data = x_data.loc[y_data.index] + X_metadata = X_metadata.loc[y_data.index] + return x_data, y_data, X_metadata + + +def _define_param_grid( + x_data: pd.DataFrame, + y_data: pd.Series | pd.DataFrame, + hpt_tuning_param_grid: dict, + l1_ratio: float | None = None, +) -> dict: + """Define the grids of hyper-parameters.""" + param_grid_values = {} + for param_name, hpt_tuning_param_value in hpt_tuning_param_grid.items(): + # Check if the argument has a get_alpha_grid method (for AlphaGridElasticNet) + if hasattr(hpt_tuning_param_value, "get_alpha_grid"): + # This is currently only available for the elastic net + param_grid_values[param_name] = hpt_tuning_param_value.get_alpha_grid(X=x_data, y=y_data, l1_ratio=l1_ratio) + else: + # Store the list of parameter values to visit in grid search + param_grid_values[param_name] = hpt_tuning_param_value + return param_grid_values + + +def _param_search( + param_grid: dict, + regression_model_base_instance: RegressionModel, + x_data: pd.DataFrame, + y_data: pd.DataFrame | pd.Series, + X_metadata: pd.DataFrame, + hpt_tuning_cv_split: Callable, + hpt_tuning_score: RegressionMetricType | None, + ensembling: bool = True, + ensembling_save_models_to_disk: bool = False, + ensembling_output_path: Path | None = None, +) -> dict: + """Perform grid search with cross-validation.""" + if hpt_tuning_score is None: + raise ValueError("A score has to be provided for the grid search.") + + models_params = define_model_params(param_grid=param_grid) + + best_score = -np.inf + best_model = None + best_ensemble_models = {} + all_results = [] + + # Iterate over all combinations of hyperparameters + for current_params in models_params: + fold_scores = [] + fold_models = {} + + # Manually iterate over the cross-validation splits + hpt_cv_splits = hpt_tuning_cv_split(X_metadata=X_metadata) + for i, (train_index, val_index) in enumerate(hpt_cv_splits): + X_train, X_val = x_data.iloc[train_index], x_data.iloc[val_index] + y_train, y_val = y_data.iloc[train_index], y_data.iloc[val_index] + + # Copy the model, set params and train + model = copy.deepcopy(regression_model_base_instance) + model.set_params(**current_params) + model.fit(X=X_train, y=y_train, X_val=X_val, y_val=y_val) + + # Predict on the validation fold + y_pred = pd.Series(model.predict(X_val), index=X_val.index) + + # Reformat y_val as a series if it is a dataframe with one column + if isinstance(y_data, pd.DataFrame) and y_val.shape[1] == 1: + y_val = y_val.iloc[:, 0] + + # Calculate the score per perturbation + score = performance_metric_wrapper( + y_true=y_val, y_pred=y_pred, metric=hpt_tuning_score, per_perturbation=True + ) + + # Handle greater is better or lower is better metrics + if hpt_tuning_score in {"mse", "mae"}: + score = -score + + fold_scores.append(score) + if ensembling: + fold_models[f"{FOLD_PREFIX}{i}"] = model + + # Compute average score across folds + mean_cv_score = float(np.mean(fold_scores)) + + # Store results + all_results.append({"params": current_params, "mean_test_score": mean_cv_score, "cv_scores": fold_scores}) + + # Update the best score and model if the current score is better + if mean_cv_score > best_score: + best_score = mean_cv_score + best_model = copy.deepcopy(regression_model_base_instance) + best_model.set_params(**current_params) + if ensembling: + best_ensemble_models = fold_models + + # Refit the best model on the full dataset with the best parameters + if best_model is None: + raise RuntimeError("No best model found. The grid search was empty.") + + best_model.fit(x_data, y_data) + + # Save all the models for each fold to the disk + best_ensemble_models_paths = {} + if ensembling and ensembling_save_models_to_disk and ensembling_output_path is not None: + ensembling_output_path.mkdir(parents=True, exist_ok=True) + for key, model in best_ensemble_models.items(): + perturbation_name = X_metadata[PERTURBATION_INDEX].iloc[0].replace("/", "") + model_path = ensembling_output_path / f"{perturbation_name}_model_{key}.pkl" + save_pickle(model, model_path) + best_ensemble_models_paths[key] = model_path + del best_ensemble_models + return { + "best_estimator_": best_model, + "cv_results_": all_results, + "best_ensemble_models_": best_ensemble_models_paths, + } + + return { + "best_estimator_": best_model, + "cv_results_": all_results, + "best_ensemble_models_": best_ensemble_models, + } + + +def _fit_single_label( + x_data: pd.DataFrame, + y_data: pd.Series | pd.DataFrame, + X_metadata: pd.DataFrame, + regression_model_base_instance: RegressionModel, + hpt_tuning_param_grid: dict | None, + hpt_tuning_cv_split: Callable | None, + hpt_tuning_score: RegressionMetricType | None, + ensembling: bool = True, + ensembling_save_models_to_disk: bool = False, + ensembling_output_path: Path | None = None, +) -> dict | dict[str, Any]: + """Fit the model on a single label. + + Note: this function is also used for the multi-label approach where the y_data + is a DataFrame with multiple columns. + """ + if isinstance(regression_model_base_instance, KnnRegressor) and not isinstance(y_data, pd.DataFrame): + raise TypeError("The KnnRegressor is only supported for multi-label regression.") + + # Drop nans in the single label approach + x_data, y_data, X_metadata = _drop_missing_values_if_any(x_data, y_data, X_metadata) + + # Fit with or without hyperparameter tuning + hpt_cv = hpt_tuning_cv_split is not None + + if hpt_cv: + if hpt_tuning_param_grid is None: + raise ValueError("A grid of hyper-parameters should be provided for the grid search.") + if hpt_tuning_cv_split is None: + raise ValueError("A split function for the hyper-parameter tuning should be provided.") + + # Define the grid of parameters to test + param_grid_values = _define_param_grid( + x_data=x_data, + y_data=y_data, + hpt_tuning_param_grid=hpt_tuning_param_grid, + l1_ratio=( + regression_model_base_instance.l1_ratio + if isinstance(regression_model_base_instance, ElasticNet) + else None + ), + ) + + # Perform grid search + grid = _param_search( + param_grid=param_grid_values, + regression_model_base_instance=regression_model_base_instance, + x_data=x_data, + y_data=y_data, + X_metadata=X_metadata, + hpt_tuning_cv_split=hpt_tuning_cv_split, + hpt_tuning_score=hpt_tuning_score, + ensembling=ensembling, + ensembling_save_models_to_disk=ensembling_save_models_to_disk, + ensembling_output_path=ensembling_output_path, + ) + else: + # Fit without CV - create a copy to avoid mutating the base instance + model = copy.deepcopy(regression_model_base_instance) + model.fit(x_data, y_data) + grid = { + "best_estimator_": model, + "cv_results_": None, + } + + return grid + + +def _fit_one_perturbation( + x_data: pd.DataFrame, + y_data: pd.Series | pd.DataFrame, + X_metadata: pd.DataFrame, + regression_model_base_instance: RegressionModel, + hpt_tuning_param_grid: dict | None, + hpt_tuning_cv_split: Callable | None, + hpt_tuning_score: RegressionMetricType | None, + ensembling: bool = True, + ensembling_save_models_to_disk: bool = False, + ensembling_output_path: Path | None = None, + pbar: tqdm | None = None, +) -> dict[str, Any]: + """Fit a model for a single perturbation.""" + # Extract the label name + label = str(y_data.name) + if pbar is not None: + pbar.set_description(f"Perturbation: {label:<25}") + + # Add the perturbation to the metadata + X_metadata[PERTURBATION_INDEX] = label + + # Store the perturbation name in a multi index of all dataframes + y_data = _add_perturbation_index(df=y_data, perturbation_name=label) + x_data = _add_perturbation_index(df=x_data, perturbation_name=label) + X_metadata = _add_perturbation_index(df=X_metadata, perturbation_name=label) + + # Used for stratification in binary classification tasks + X_metadata["y"] = y_data + + # Do the hyper-parameter tuning with grid search + return _fit_single_label( + x_data=x_data, + y_data=y_data, + X_metadata=X_metadata, + regression_model_base_instance=regression_model_base_instance, + hpt_tuning_param_grid=hpt_tuning_param_grid, + hpt_tuning_cv_split=hpt_tuning_cv_split, + hpt_tuning_score=hpt_tuning_score, + ensembling=ensembling, + ensembling_save_models_to_disk=ensembling_save_models_to_disk, + ensembling_output_path=ensembling_output_path, + ) + + +def define_model_params(param_grid: dict, seed: int | None = None) -> list[dict]: + """Define model parameters. + + Parameters + ---------- + param_grid : dict + Dictionary with the parameters to search. + seed : int | None + Seed for reproducibility, only used if "random" search type is being requested, by default None. + + Returns + ------- + list[dict] + List of parameter dictionaries. + """ + if seed is not None: + np.random.seed(seed) + + parameter_names = list(param_grid.keys()) + + param_combinations: list[tuple] | np.ndarray = list(product(*param_grid.values())) + + # Convert each row of values into a dictionary + models_params = [dict(zip(parameter_names, model_values, strict=False)) for model_values in param_combinations] + + return models_params diff --git a/src/leap/regression_models/__init__.py b/src/leap/regression_models/__init__.py new file mode 100644 index 0000000..9bc0c75 --- /dev/null +++ b/src/leap/regression_models/__init__.py @@ -0,0 +1,83 @@ +"""Prediction models for LEAP.""" + +from typing import Any, Protocol, runtime_checkable + +import numpy as np +import pandas as pd +from lightgbm import LGBMRegressor +from skglm import ElasticNet + +from .knn_regressor import KnnRegressor +from .mlp_regressor import TorchMLPRegressor +from .utils import AlphaGridElasticNet + + +@runtime_checkable +class RegressionModel(Protocol): + """Protocol defining the interface for regression models in LEAP. + + All regression models must implement fit(), predict() and set_params() methods with these signatures. This protocol + works with external libraries (ElasticNet, LGBMRegressor) and custom implementations alike. + """ + + def fit(self, X: pd.DataFrame, y: pd.Series | pd.DataFrame, **kwargs: Any) -> None: + """Fit the regression model. + + Parameters + ---------- + X : pd.DataFrame + Training features. + y : pd.Series | pd.DataFrame + Training targets. + """ + ... + + def predict(self, X: pd.DataFrame) -> np.ndarray: + """Make predictions using the trained model. + + Parameters + ---------- + X : pd.DataFrame + Features to predict on. + + Returns + ------- + np.ndarray + Predictions. + """ + ... + + def set_params(self, **kwargs: Any) -> "RegressionModel": + """Set parameters for this estimator. + + Returns + ------- + RegressionModel + The instance itself. + """ + ... + + def get_params(self, deep: bool = True) -> dict[str, Any]: + """Get parameters for this estimator. + + Parameters + ---------- + deep : bool + If True, will return the parameters for this estimator and contained subobjects that are estimators. + + Returns + ------- + dict[str, Any] + Parameter names mapped to their values. + """ + ... + + +__all__ = [ + "AlphaGridElasticNet", + "ElasticNet", + "KnnRegressor", + "LGBMRegressor", + "RegressionModel", + "TorchMLPRegressor", +] diff --git a/src/leap/regression_models/knn_regressor.py b/src/leap/regression_models/knn_regressor.py new file mode 100644 index 0000000..b912649 --- /dev/null +++ b/src/leap/regression_models/knn_regressor.py @@ -0,0 +1,66 @@ +"""KNN Regression Model.""" + +from typing import Any + +import numpy as np +import pandas as pd +from sklearn.base import BaseEstimator +from sklearn.neighbors import KNeighborsRegressor + + +class KnnRegressor(BaseEstimator): + """KNN Regression Model. + + This KNN model is using the KNeighborsRegressor from scikit-learn in a way that allows having nan values in the + target variable. The model is fit for each target on the X data that correspond to non-nan values in the target + variable. + + Parameters + ---------- + n_sample_neighbors : int + Number of neighbors to use. + weights : str + Weight function used in prediction. + n_jobs : int + Number of jobs to run in parallel. Default is 1. + """ + + def __init__(self, n_sample_neighbors: int, weights: str, n_jobs: int = 1): + super().__init__() + self.n_sample_neighbors = n_sample_neighbors + self.weights = weights + self.n_jobs = n_jobs + self.X_train: pd.DataFrame + self.y_train: pd.DataFrame + + def fit(self, X: pd.DataFrame, y: pd.DataFrame, **kwargs: Any) -> None: + """Fit method for the KnnRegressor. + + KNeighborsRegressor is a single label regression model but instantiating and storing a unique model per + perturbation (times N splits and M repeats) is way too expensive in terms of memory and time. Therefore we + suggest to only store the training data at training time and to fit the knn at every inference call. + This is possible as KNN is a non-parametric model and inference is fast. + """ + self.X_train = X + self.y_train = y + + def predict(self, X_pred: pd.DataFrame) -> np.ndarray: + """Predict method. + + The fact that the `fit` method of KNeighborsRegressor is called here makes it more memory efficient than storing + a unique model per perturbation. + """ + pred_values = [] + for col_target in self.y_train.columns: + y_train_col = self.y_train[col_target].dropna() + X_train_col = self.X_train.loc[y_train_col.index] + + # hack to avoid having n_sample_neighbors higher than the number of samples + # Note: the spearman will then be 0 by our definition of the metric when + # the prediction is constant. + n_sample_neighbors = min(self.n_sample_neighbors, len(y_train_col)) + + knn = KNeighborsRegressor(n_neighbors=n_sample_neighbors, weights=self.weights, n_jobs=self.n_jobs) + knn.fit(X_train_col, y_train_col) + pred_values.append(knn.predict(X_pred).tolist()) + return np.array(pred_values).T diff --git a/src/leap/regression_models/mlp_regressor.py b/src/leap/regression_models/mlp_regressor.py new file mode 100644 index 0000000..4475b15 --- /dev/null +++ b/src/leap/regression_models/mlp_regressor.py @@ -0,0 +1,731 @@ +"""MLPRegressor using pytorch so that it can be accelerated with GPUs.""" + +from collections.abc import Callable +from functools import partial +from typing import Any, Literal + +import numpy as np +import pandas as pd +import torch +from sklearn.preprocessing import MinMaxScaler, RobustScaler, StandardScaler +from torch import nn, optim +from torch.utils.data import DataLoader, TensorDataset, random_split + +from leap.metrics.regression_metrics import performance_metric_wrapper +from leap.utils.device import get_device +from leap.utils.seed import seed_everything + +from .utils import SpearmanLoss + + +class TorchMLPRegressor: + """Multi-layer Perceptron Regressor implemented in PyTorch with GPU support. + + This implementation provides a flexible neural network regressor with support for early stopping, learning rate + scheduling, dropout, and multiple loss functions. + + Parameters + ---------- + hidden_layer_sizes : tuple + The ith element represents the number of neurons in the ith hidden layer. + activation : Literal["relu", "tanh"] + Activation function for the hidden layers. + learning_rate_init : float + The initial learning rate for the optimizer. + max_epochs : int + Maximum number of epochs for training. + batch_size : int + Number of samples per batch for training. + dropout_rate : float + The dropout rate applied after each hidden layer. Must be in [0, 1). + random_seed : int + Random seed for reproducibility. + early_stopping_use : bool + Whether to use early stopping based on validation performance. + early_stopping_split : float + Fraction of training data to use for validation if early stopping is + enabled and no validation set is provided. Must be in (0, 1). + early_stopping_patience : int + Number of epochs with no improvement after which training will be stopped. + early_stopping_delta : float + Minimum change in the monitored metric to qualify as an improvement. + optimizer_type : Literal["adam", "sgd"] + The optimizer to use for training. + weight_decay : float + Weight decay (L2 penalty) for the optimizer. + learning_rate_scheduler : bool + Whether to use a learning rate scheduler that reduces LR on plateau. + scheduler_factor : float + Factor by which the learning rate will be reduced. + scheduler_patience : int + Number of epochs with no improvement after which learning rate will be reduced. + scheduler_threshold : float + Threshold for measuring the new optimum for the scheduler. + metric : Literal["spearman", "mse"] + Metric function to evaluate the model during early stopping. + Note: Despite the name, "spearman" actually uses Pearson correlation. + scaler_name : Literal["standard", "minmax", "robust"] | None + Scaler to use for feature normalization. If None, no scaling is applied. + loss_function_name : Literal["mse", "spearman", "binary_cross_entropy"] + Loss function to use for training. + Note: "spearman" actually uses Pearson correlation. + device : str | None + Device to run training on ('cpu', 'cuda', or 'mps'). + If None, automatically detects best available device. + + Attributes + ---------- + model : nn.Module + The PyTorch neural network model. + optimizer : optim.Optimizer + The optimizer used for training. + scheduler : optim.lr_scheduler.ReduceLROnPlateau + Learning rate scheduler (if enabled). + scaler : StandardScaler | MinMaxScaler | RobustScaler | None + The fitted scaler for feature normalization. + criterion : nn.Module + The loss function module. + loss_history_train : list[float] + Training loss history per batch. + loss_history_val : list[float] + Validation loss history per epoch (if early stopping is used). + metric_history_val : list[float] + Validation metric history per epoch (if early stopping is used). + + + Raises + ------ + ValueError + If the parameters are invalid. + + Notes + ----- + - This implementation expects target data (y) to have a MultiIndex with a "perturbation" level when using early + stopping. + - The model is automatically moved to the appropriate device (CUDA, MPS, or CPU) based on availability. + - BCEWithLogitsLoss includes sigmoid activation internally, so no sigmoid is added to the network when using binary + cross-entropy loss. + + Examples + -------- + >>> import pandas as pd + >>> import numpy as np + >>> from leap.regression_models import TorchMLPRegressor + >>> + >>> # Create sample data + >>> X_train = pd.DataFrame(np.random.randn(100, 10)) + >>> y_train = pd.Series(np.random.randn(100)) + >>> + >>> # Train model + >>> model = TorchMLPRegressor(hidden_layer_sizes=(64, 32), max_epochs=50, early_stopping_use=False) + >>> model.fit(X_train, y_train) + >>> + >>> # Make predictions + >>> X_test = pd.DataFrame(np.random.randn(20, 10)) + >>> predictions = model.predict(X_test) + """ + + def __init__( + self, + hidden_layer_sizes: tuple = (100,), + activation: Literal["relu", "tanh"] = "relu", + learning_rate_init: float = 0.001, + max_epochs: int = 200, + batch_size: int = 64, + dropout_rate: float = 0.0, + random_seed: int = 0, + early_stopping_use: bool = False, + early_stopping_split: float = 0.2, + early_stopping_patience: int = 20, + early_stopping_delta: float = 0.0001, + optimizer_type: Literal["adam", "sgd"] = "adam", + weight_decay: float = 1e-5, + learning_rate_scheduler: bool = False, + scheduler_factor: float = 0.1, + scheduler_patience: int = 10, + scheduler_threshold: float = 0.001, + metric: Literal["spearman", "mse"] = "spearman", + scaler_name: Literal["standard", "minmax", "robust"] | None = "robust", + loss_function_name: Literal["mse", "spearman", "binary_cross_entropy"] = "mse", + device: str | None = None, + ): + # Validate parameters + if not hidden_layer_sizes: + raise ValueError("hidden_layer_sizes must contain at least one layer") + if not 0 <= dropout_rate < 1: + raise ValueError(f"dropout_rate must be in [0, 1), got {dropout_rate}") + if not 0 < early_stopping_split < 1: + raise ValueError(f"early_stopping_split must be in (0, 1), got {early_stopping_split}") + if max_epochs <= 0: + raise ValueError(f"max_epochs must be positive, got {max_epochs}") + if batch_size <= 0: + raise ValueError(f"batch_size must be positive, got {batch_size}") + + # Store the input parameters as instance attributes + self.hidden_layer_sizes = hidden_layer_sizes + self.activation = activation + self.learning_rate_init = learning_rate_init + self.max_epochs = max_epochs + self.batch_size = batch_size + self.dropout_rate = dropout_rate + self.random_seed = random_seed + self.early_stopping_use = early_stopping_use + self.early_stopping_split = early_stopping_split + self.early_stopping_patience = early_stopping_patience + self.early_stopping_delta = early_stopping_delta + self.optimizer_type = optimizer_type + self.weight_decay = weight_decay + self.learning_rate_scheduler = learning_rate_scheduler + self.scheduler_factor = scheduler_factor + self.scheduler_patience = scheduler_patience + self.scheduler_threshold = scheduler_threshold + self.n_epoch = 0 + self.loss_history_train: list[float] = [] + self.loss_history_val: list[float] = [] + self.metric_history_val: list[float] = [] + self.loss_function_name = loss_function_name + self.scaler_name = scaler_name + + # Set the metric function + self.metric = partial(performance_metric_wrapper, metric=metric, per_perturbation=True) + self.metric_direction = -1 if metric == "mse" else 1 + + # Set the random seeds for reproducibility + seed_everything(self.random_seed) + + # Set device (CPU, CUDA, or MPS) + self.device = get_device(device) + + # Placeholder for the PyTorch model and optimizer + self.model: nn.Module + self.optimizer: optim.Optimizer + self.scheduler: optim.lr_scheduler.ReduceLROnPlateau + + # Scaler and loss function + self.scaler, self.criterion = self._get_scaler_and_loss() + + def _get_scaler_and_loss(self) -> tuple[StandardScaler | MinMaxScaler | RobustScaler | None, nn.Module]: + """Initialize the scaler and loss function. + + This becomes necessary as these are not args but need to be re-defined when set_params is called (used for CV). + + Returns + ------- + tuple[StandardScaler | MinMaxScaler | RobustScaler | None, nn.Module] + A tuple containing: + - scaler: Optional sklearn scaler (StandardScaler, MinMaxScaler, RobustScaler, or None) + - criterion: Loss function as an nn.Module (SpearmanLoss, BCEWithLogitsLoss, or MSELoss) + + Raises + ------ + NotImplementedError + If the loss function name is not supported. + ValueError + If the scaler name is not supported. + """ + # Loss function + criterion: nn.Module + if self.loss_function_name == "spearman": + criterion = SpearmanLoss() + elif self.loss_function_name == "binary_cross_entropy": + criterion = nn.BCEWithLogitsLoss() + elif self.loss_function_name == "mse": + criterion = nn.MSELoss() + else: + raise NotImplementedError(f"Loss function '{self.loss_function_name}' is not supported or implemented.") + + # Scaler + if self.scaler_name is None: + scaler = None + elif self.scaler_name == "standard": + scaler = StandardScaler() + elif self.scaler_name == "minmax": + scaler = MinMaxScaler() + elif self.scaler_name == "robust": + scaler = RobustScaler() + else: + raise ValueError(f"Unsupported scaler: {self.scaler_name}") + + return scaler, criterion + + def _create_dataloader( + self, X: pd.DataFrame, y: pd.Series | pd.DataFrame, device: str, batch_size: int, shuffle: bool = True + ) -> DataLoader: + """Create a PyTorch DataLoader from features and targets. + + Parameters + ---------- + X : pd.DataFrame + Feature data. + y : pd.Series | pd.DataFrame + Target data. + device : str + Device to load the tensors to ('cpu', 'cuda', or 'mps'). + batch_size : int + Size of batches. + shuffle : bool + Whether to shuffle the data. + + Returns + ------- + DataLoader + PyTorch DataLoader containing the data. + """ + return DataLoader( + TensorDataset( + torch.tensor(X.to_numpy(), dtype=torch.float32).to(device), + torch.tensor(y.to_numpy(), dtype=torch.float32).to(device), + ), + batch_size=batch_size, + shuffle=shuffle, + pin_memory=False, + ) + + def _build_model(self, input_size: int, output_size: int) -> None: + """Build the PyTorch model. + + Parameters + ---------- + input_size : int + Number of input features. + output_size : int + Number of output features. + + Raises + ------ + ValueError + If an unsupported activation function or optimizer type is provided. + """ + layers: list[nn.Module] = [] + in_features = input_size + + # Select the activation function based on user input + if self.activation == "relu": + activation_fn: Callable[[], nn.Module] = nn.ReLU + elif self.activation == "tanh": + activation_fn = nn.Tanh + else: + raise ValueError(f"Unsupported activation function: {self.activation}") + + # Add hidden layers with specified sizes and activation functions + for hidden_size in self.hidden_layer_sizes: + layers.extend( + [ + nn.Linear(in_features, hidden_size), + activation_fn(), + nn.Dropout(self.dropout_rate) if self.dropout_rate > 0.0 else nn.Identity(), + ] + ) + in_features = hidden_size + + # Add output layer + layers.append(nn.Linear(in_features, output_size)) + + # Create sequential model from layers + self.model = nn.Sequential(*layers) + + # Select optimizer + if self.optimizer_type == "adam": + self.optimizer = optim.Adam( + self.model.parameters(), lr=self.learning_rate_init, weight_decay=self.weight_decay + ) + elif self.optimizer_type == "sgd": + self.optimizer = optim.SGD( + self.model.parameters(), lr=self.learning_rate_init, weight_decay=self.weight_decay, momentum=0.9 + ) + else: + raise ValueError(f"Unsupported optimizer type: {self.optimizer_type}") + + # Set up learning rate scheduler, if enabled + if self.learning_rate_scheduler: + self.scheduler = optim.lr_scheduler.ReduceLROnPlateau( + self.optimizer, + mode="min" if self.metric_direction == -1 else "max", + factor=self.scheduler_factor, + patience=self.scheduler_patience, + threshold=self.scheduler_threshold, + ) + + def _get_val_tensors( + self, X_val: pd.DataFrame, y_val: pd.Series | pd.DataFrame + ) -> tuple[torch.Tensor, torch.Tensor, pd.Index]: + """Get validation tensors. + + Parameters + ---------- + X_val : pd.DataFrame + Validation features. + y_val : pd.Series | pd.DataFrame + Validation targets. Must have a MultiIndex with a "perturbation" level. + + Returns + ------- + tuple[torch.Tensor, torch.Tensor, pd.Index] + Validation features tensor, validation targets tensor, and perturbations index. + + Raises + ------ + KeyError + If y_val index doesn't have a "perturbation" level. + """ + # Convert validation data to PyTorch tensors + X_val_array = self.scaler.transform(X_val) if self.scaler is not None else X_val.to_numpy() + X_val_tensor = torch.tensor(X_val_array, dtype=torch.float32).to(self.device) + + try: + val_perturbations = y_val.index.get_level_values("perturbation") + except KeyError as e: + raise KeyError( + "y_val must have a MultiIndex with a 'perturbation' level for early stopping. " + f"Got index levels: {y_val.index.names}" + ) from e + + y_val_tensor = torch.tensor(y_val.to_numpy(), dtype=torch.float32).to(self.device) + return X_val_tensor, y_val_tensor, val_perturbations + + def _get_train_val_split( + self, X: pd.DataFrame, y: pd.Series | pd.DataFrame + ) -> tuple[DataLoader, torch.Tensor, torch.Tensor, pd.Index]: + """Create training and validation dataloaders. + + Parameters + ---------- + X : pd.DataFrame + Feature data. + y : pd.Series | pd.DataFrame + Target data. Must have a MultiIndex with a "perturbation" level. + + Returns + ------- + tuple[DataLoader, torch.Tensor, torch.Tensor, pd.Index] + Training dataloader, validation features tensor, validation targets tensor, and validation perturbations + index. + + Raises + ------ + KeyError + If y index doesn't have a "perturbation" level. + """ + # Convert input and target data to PyTorch tensors + X_tensor = torch.tensor(X.to_numpy(), dtype=torch.float32).to(self.device) + y_tensor = torch.tensor(y.to_numpy(), dtype=torch.float32).to(self.device) + + # Split the data into training and validation sets + dataset_size = len(X_tensor) + val_size = int(self.early_stopping_split * dataset_size) + train_size = dataset_size - val_size + indices = torch.tensor(range(dataset_size)) + train_dataset, val_dataset = random_split( + TensorDataset(X_tensor, y_tensor, indices), + [train_size, val_size], + generator=torch.Generator().manual_seed(self.random_seed), + ) + train_dataloader = DataLoader(train_dataset, batch_size=self.batch_size, shuffle=True, pin_memory=False) + + # Extract X_val and y_val from val_dataset + # For validation data, we don't need a DataLoader since: + # 1. All data fits in memory + # 2. We don't need batching since we're not training + # 3. We evaluate on the full validation set at once + X_val = torch.stack([val_dataset[i][0] for i in range(len(val_dataset))]) + y_val = torch.stack([val_dataset[i][1] for i in range(len(val_dataset))]) + val_indices = torch.stack([val_dataset[i][2] for i in range(len(val_dataset))]) + + try: + val_perturbations = y.iloc[val_indices.cpu().numpy()].index.get_level_values("perturbation") + except KeyError as e: + raise KeyError( + "y must have a MultiIndex with a 'perturbation' level for early stopping. " + f"Got index levels: {y.index.names}" + ) from e + + # Clean GPU memory + del X_tensor, y_tensor, train_dataset, val_dataset, val_indices + + return train_dataloader, X_val, y_val, val_perturbations + + def _early_stopping( + self, + epoch: int, + model_gpu: nn.Module, + patience_counter: int, + best_metric: float, + X_val: torch.Tensor, + y_val: torch.Tensor, + val_perturbations: pd.Index, + ) -> tuple[int, float, float, dict | None]: + """Early stopping logic. + + Parameters + ---------- + epoch : int + Current epoch number. + model_gpu : nn.Module + Model on GPU. + patience_counter : int + Current patience counter. + best_metric : float + Best metric achieved so far. + X_val : torch.Tensor + Validation features. + y_val : torch.Tensor + Validation targets. + val_perturbations : pd.Index + Validation perturbations index. + + Returns + ------- + tuple[int, float, float, dict | None] + Updated patience counter, validation metric, best metric, and best model state dict. + """ + # Create placeholder for the best model state + best_model_state: dict | None = None + + # Validation phase + model_gpu.eval() # Set model to evaluation mode + with torch.no_grad(): # Disable gradient computation + # Use no dataloader to avoid extra overhead when doing per-perturbation predictions and metrics + # + all data fits in memory + val_predictions = model_gpu(X_val) # Forward pass + val_targets = y_val.view(-1, 1) # Reshape target to match output + val_loss = self.criterion(val_predictions, val_targets) # Loss + + # Compute validation metric per perturbation + val_targets_series = pd.Series( + val_targets.cpu().numpy().flatten(), + index=pd.MultiIndex.from_arrays( + [range(len(val_targets)), val_perturbations], names=["sample", "perturbation"] + ), + ) + val_predictions_series = pd.Series( + val_predictions.cpu().numpy().flatten(), + index=pd.MultiIndex.from_arrays( + [range(len(val_targets)), val_perturbations], names=["sample", "perturbation"] + ), + ) + + val_metric = self.metric(val_targets_series, val_predictions_series) + self.loss_history_val.append(val_loss.item()) + self.metric_history_val.append(val_metric) + + # Early stopping logic + val_metric *= self.metric_direction + if val_metric > best_metric + self.early_stopping_delta: + best_metric = val_metric + patience_counter = 0 + best_model_state = model_gpu.state_dict() + else: + patience_counter += 1 + + return patience_counter, val_metric, best_metric, best_model_state + + def fit( # noqa: PLR0912, PLR0915 + self, + X: pd.DataFrame, + y: pd.Series | pd.DataFrame, + X_val: pd.DataFrame | None = None, + y_val: pd.Series | pd.DataFrame | None = None, + **kwargs: Any, + ) -> None: + """Fit the model to the training data. + + Parameters + ---------- + X : pd.DataFrame + Training data features. + y : pd.Series | pd.DataFrame + Training data target values. If early_stopping_use is True, must have a MultiIndex with a "perturbation" + level. + X_val : pd.DataFrame | None + Validation data features for early stopping. If early_stopping_use is True and this is None, a validation + split will be created from the training data. + y_val : pd.Series | pd.DataFrame | None + Validation data target values for early stopping. If early_stopping_use is True and this is None, a + validation split will be created from the training data. Must have a MultiIndex with a "perturbation" level. + + Raises + ------ + ValueError + If early_stopping_use is True and only one of X_val or y_val is provided (both must be provided together or + neither). + RuntimeError + If an internal consistency check fails during training. + """ + # Set the random seeds for reproducibility + seed_everything(self.random_seed) + + # Scale features (create a copy to avoid mutating input) + X_scaled = pd.DataFrame( + (self.scaler.fit_transform(X) if self.scaler is not None else X.to_numpy()), + index=X.index, + columns=X.columns, + ) + + # Build the model if not already built + if not hasattr(self, "model"): + # Determine input and output sizes + input_size = X_scaled.shape[1] + output_size = y.shape[1] if len(y.shape) > 1 else 1 + self._build_model(input_size, output_size) + + # Move model to appropriate device (CPU or GPU) + model_gpu = self.model.to(self.device) + + # PREPARE DATA + if self.early_stopping_use and X_val is None and y_val is None: + train_dataloader, X_val_tensor, y_val_tensor, val_perturbations = self._get_train_val_split(X_scaled, y) + else: + train_dataloader = self._create_dataloader( + X=X_scaled, y=y, device=self.device, batch_size=self.batch_size, shuffle=True + ) + if self.early_stopping_use: + # Validate that both X_val and y_val are provided together + if X_val is None or y_val is None: + raise ValueError( + "When early_stopping_use is True and validation data is provided, " + "both X_val and y_val must be provided together. " + f"Got X_val={'provided' if X_val is not None else 'None'}, " + f"y_val={'provided' if y_val is not None else 'None'}." + ) + X_val_tensor, y_val_tensor, val_perturbations = self._get_val_tensors(X_val, y_val) + else: + val_perturbations = None + + # Initialize best metric for early stopping + if self.early_stopping_use: + best_metric = -np.inf + patience_counter = 0 + best_model_state: dict | None = None + + # TRAINING LOOP + for epoch in range(self.max_epochs): + self.n_epoch = epoch + # Training phase + model_gpu.train() # Set model to training mode + train_losses = [] # Initialize list to track training losses + + for batch in train_dataloader: + X_batch, y_batch = batch[0], batch[1] + self.optimizer.zero_grad() # Zero the gradients + outputs = model_gpu(X_batch) # Forward pass + y_batch = y_batch.view(-1, 1) # Reshape target to match output + loss = self.criterion(outputs, y_batch) # Compute loss + loss.backward() # Backpropagation + self.optimizer.step() # Update weights + train_losses.append(loss.item()) # Append current batch loss + self.loss_history_train.append(loss.item()) + + # Handle early stopping + if self.early_stopping_use: + # Internal consistency check + if val_perturbations is None: + raise RuntimeError( + "Internal error: val_perturbations is None despite early_stopping_use=True. " + "This indicates a bug in the data preparation logic." + ) + patience_counter, val_metric, best_metric, best_model_state = self._early_stopping( + epoch=epoch, + model_gpu=model_gpu, + patience_counter=patience_counter, + best_metric=best_metric, + X_val=X_val_tensor, + y_val=y_val_tensor, + val_perturbations=val_perturbations, + ) + + # Learning rate scheduling (when early stopping is used) + if self.learning_rate_scheduler: + self.scheduler.step(metrics=val_metric) + + # Check if patience exceeded + if patience_counter >= self.early_stopping_patience: + break + elif self.learning_rate_scheduler: + # Learning rate scheduling without early stopping + # Use training loss as metric + self.scheduler.step(metrics=np.mean(train_losses)) + + # Load the best model state if available + if self.early_stopping_use and best_model_state is not None: + model_gpu.load_state_dict(best_model_state) + + # Move model back to CPU for prediction consistency + self.model = model_gpu.cpu() + + # Clean up memory + if self.early_stopping_use: + del X_val_tensor, y_val_tensor, best_model_state + del model_gpu, train_dataloader, outputs, loss + torch.cuda.empty_cache() + torch.mps.empty_cache() + + def predict(self, X: pd.DataFrame) -> np.ndarray: + """Predict using the trained model. + + Parameters + ---------- + X : pd.DataFrame + Input features. + + Returns + ------- + np.ndarray + Predictions as a 1D array. + """ + # Scale input data + X_array = self.scaler.transform(X) if self.scaler is not None else X.to_numpy() + + # Convert input data to PyTorch tensor + # Model is on CPU after training, so put data on CPU too + X_tensor = torch.tensor(X_array, dtype=torch.float32) + + # Set model to evaluation mode + self.model.eval() + + # Disable gradient computation for prediction + with torch.no_grad(): + predictions = self.model(X_tensor) + + # Return predictions as a NumPy array + return predictions.view(-1).cpu().numpy() + + def get_params(self, deep: bool = True) -> dict[str, Any]: + """Get parameters for this estimator.""" + return { + "hidden_layer_sizes": self.hidden_layer_sizes, + "activation": self.activation, + "learning_rate_init": self.learning_rate_init, + "max_epochs": self.max_epochs, + "batch_size": self.batch_size, + "dropout_rate": self.dropout_rate, + "random_seed": self.random_seed, + "early_stopping_use": self.early_stopping_use, + "early_stopping_split": self.early_stopping_split, + "early_stopping_patience": self.early_stopping_patience, + "early_stopping_delta": self.early_stopping_delta, + "optimizer_type": self.optimizer_type, + "weight_decay": self.weight_decay, + "learning_rate_scheduler": self.learning_rate_scheduler, + "scheduler_factor": self.scheduler_factor, + "scheduler_patience": self.scheduler_patience, + "scheduler_threshold": self.scheduler_threshold, + "metric": self.metric, + "scaler_name": self.scaler_name, + "loss_function_name": self.loss_function_name, + } + + def set_params(self, **kwargs: Any) -> "TorchMLPRegressor": + """Set parameters for this estimator.""" + for parameter, value in kwargs.items(): + setattr(self, parameter, value) + self.scaler, self.criterion = self._get_scaler_and_loss() + return self + + def __del__(self) -> None: + """Cleanup method to free GPU memory when the object is destroyed.""" + if hasattr(self, "model"): + del self.model + if "torch" in globals() and getattr(torch, "cuda", None) is not None and torch.cuda.is_available(): + torch.cuda.empty_cache() + if "torch" in globals() and getattr(torch, "mps", None) is not None and torch.mps.is_available(): + torch.mps.empty_cache() diff --git a/src/leap/regression_models/utils.py b/src/leap/regression_models/utils.py new file mode 100644 index 0000000..368ca02 --- /dev/null +++ b/src/leap/regression_models/utils.py @@ -0,0 +1,92 @@ +"""Elastic Net model for LEAP. + +The ElasticNet model is already implemented in skglm, so here we just define a utils for the alpha grid search. +""" + +import numpy as np +import pandas as pd +import torch +from sklearn.linear_model._coordinate_descent import _alpha_grid +from torch import nn + + +class SpearmanLoss(nn.Module): + """Differentiable Surrogate Spearman correlation loss module. + + A PyTorch module wrapper for the differentiable Spearman correlation loss. + This computes 1 - correlation to convert it into a loss (minimization problem). + + Note + ---- + Despite the name "Spearman" being used in the configuration, this actually computes Pearson correlation (not + Spearman rank correlation). For true Spearman correlation, the inputs would need to be ranked first. + """ + + def forward(self, y_pred: torch.Tensor, y_true: torch.Tensor) -> torch.Tensor: + """Compute the Spearman loss. + + Parameters + ---------- + y_pred : torch.Tensor + Predicted values. + y_true : torch.Tensor + True target values. + + Returns + ------- + torch.Tensor + Differentiable Surrogate Spearman loss (1 - correlation). + """ + # Compute the covariance + y_true_mean = torch.mean(y_true) + y_pred_mean = torch.mean(y_pred) + cov = torch.mean((y_true - y_true_mean) * (y_pred - y_pred_mean)) + + # Compute the standard deviations (use population std, correction=0) + y_true_std = torch.std(y_true, correction=0) + y_pred_std = torch.std(y_pred, correction=0) + + # Compute the correlation + spearman_corr = cov / (y_true_std * y_pred_std + 1e-8) # Add epsilon to avoid division by zero + + # Return 1 - spearman_corr to convert it into a loss (minimization problem) + return 1 - spearman_corr + + +class AlphaGridElasticNet: + """Class for the grid of alpha parameters. + + Parameters + ---------- + alpha_min_ratio : float + The ratio to define the minimum alpha parameter. + n_alphas : int + The number of alpha parameters. + """ + + def __init__(self, alpha_min_ratio: float = 1e-3, n_alphas: int = 10): + self.alpha_min_ratio = alpha_min_ratio + self.n_alphas = n_alphas + + def get_alpha_grid(self, X: pd.DataFrame, y: pd.DataFrame, l1_ratio: float) -> list: + """Define the grid of alpha parameters. + + Parameters + ---------- + X : pd.DataFrame + The input data. + y : pd.DataFrame + The output data. + l1_ratio: float + The l1_ratio of the Elastic Net model. + + Returns + ------- + list + The list of alpha parameters. + """ + X_np = X.to_numpy() if isinstance(X, pd.DataFrame) else X + y_np = y.to_numpy().ravel() if isinstance(y, (pd.DataFrame, pd.Series)) else y + alpha_max = _alpha_grid(X_np, y_np, l1_ratio=l1_ratio, n_alphas=1)[0] + alpha_grid = list(np.logspace(np.log10(alpha_max * self.alpha_min_ratio), np.log10(alpha_max), self.n_alphas)) + return alpha_grid diff --git a/src/leap/representation_models/__init__.py b/src/leap/representation_models/__init__.py new file mode 100644 index 0000000..f35c59f --- /dev/null +++ b/src/leap/representation_models/__init__.py @@ -0,0 +1,85 @@ +"""Representation models for LEAP.""" + +from abc import ABC, abstractmethod +from typing import Any + +import numpy as np +import pandas as pd + + +class RepresentationModelBase(ABC): + """Abstract base class for representation models in LEAP. + + All representation models must implement fit(), transform() methods and define + a repr_dim attribute in their __init__ method that specifies the dimensionality + of the learned representation. + + This base class works with sklearn-based models (PCA) and custom PyTorch + implementations (AutoEncoder, MaskedAutoencoder) alike. + + Attributes + ---------- + repr_dim : int + The dimensionality of the learned representation. All subclasses must set + this attribute in their __init__ method. + + Notes + ----- + While `repr_dim` cannot be enforced at the abstract class level due to being + set in __init__, all implementations should include this attribute. The type + checker will help ensure this requirement is met. + """ + + # Type hint for the attribute that subclasses must set + repr_dim: int + + @abstractmethod + def fit(self, X: pd.DataFrame, **kwargs: Any) -> "RepresentationModelBase": + """Fit the representation model to the training data. + + Parameters + ---------- + X : pd.DataFrame + Training data. + + Returns + ------- + RepresentationModelBase + The fitted model instance. + + Raises + ------ + NotImplementedError + If the subclass does not implement fit(). + """ + raise NotImplementedError("Subclasses must implement fit()") + + @abstractmethod + def transform(self, X: pd.DataFrame) -> np.ndarray: + """Transform data using the fitted representation model. + + Parameters + ---------- + X : pd.DataFrame + Data to transform. + + Returns + ------- + np.ndarray + Transformed data in the learned representation space. + + Raises + ------ + NotImplementedError + If the subclass does not implement transform(). + """ + raise NotImplementedError("Subclasses must implement transform()") + + +# Import classes that depend on RepresentationModelBase AFTER defining it +from .auto_encoder import AutoEncoder # noqa: E402 +from .masked_auto_encoder import MaskedAutoencoder # noqa: E402 +from .pca import PCA # noqa: E402 + + +__all__ = ["PCA", "AutoEncoder", "MaskedAutoencoder", "RepresentationModelBase"] diff --git a/src/leap/representation_models/auto_encoder.py b/src/leap/representation_models/auto_encoder.py new file mode 100644 index 0000000..7db927c --- /dev/null +++ b/src/leap/representation_models/auto_encoder.py @@ -0,0 +1,425 @@ +"""AutoEncoder implementation using PyTorch.""" + +from collections.abc import Callable +from typing import Any + +import numpy as np +import pandas as pd +import torch +from loguru import logger +from sklearn.model_selection import train_test_split +from torch.utils.data import DataLoader +from tqdm import tqdm + +from leap.utils.device import get_device +from leap.utils.seed import seed_everything + +from . import RepresentationModelBase +from .utils import OmicsDataset, _initialize_early_stopping, _update_early_stopping + + +class AutoEncoder(RepresentationModelBase, torch.nn.Module): + """Representation model using Autoencoder architecture. + + Parameters + ---------- + repr_dim : int + Size of the representation dimension (bottleneck). + hidden_n_layers : int + Number of hidden layers. + hidden_n_units_first : int + Number of units of the first hidden layer. + hidden_decrease_rate : float + Decrease rate of the number of units per layer. + dropout : float | None + Dropout probability for hidden layers. If None, no dropout is applied. + activation : torch.nn.Module | None + Activation function for the hidden layers. + bias : bool + If False, the layers will not learn an additive bias. + num_epochs : int + Number of epochs when early stopping is not used. + batch_size : int + Size of minibatches for training. + learning_rate : float + Learning rate for the optimizer. + early_stopping_use : bool + Whether to use early stopping with a validation set. + max_num_epochs : int + Maximum number of epochs when using early stopping. + early_stopping_split : float + Train/val split proportion for early stopping. + early_stopping_patience : int + Number of epochs without improvement before stopping. + early_stopping_delta : float + Minimum improvement required to reset patience counter. + retrain : bool + Whether to retrain on full data after early stopping finds optimal epochs. + device : str | None + Device to run training on ('cpu', 'cuda', or 'mps'). If None, automatically detects best available device. + random_state : int + Random seed for reproducibility. + criterion : torch.nn.Module + Loss function for the autoencoder reconstruction task. + optimizer : Callable + Optimizer class to use for training. + + Attributes + ---------- + encoder : torch.nn.Module + The encoder network. + decoder : torch.nn.Module + The decoder network. + train_loss : list[float] + Training loss history. + eval_loss : list[float] + Validation loss history (if early stopping is used). + + Raises + ------ + ValueError + If early_stopping_patience >= max_num_epochs. + + Examples + -------- + >>> import pandas as pd + >>> import numpy as np + >>> from leap.representation_models import AutoEncoder + >>> + >>> X = pd.DataFrame(np.random.randn(100, 50)) + >>> ae = AutoEncoder(repr_dim=10, num_epochs=50) + >>> X_transformed = ae.fit(X) + >>> X_transformed = ae.transform(X) + >>> print(X_transformed.shape) + (100, 10) + """ + + def __init__( + self, + repr_dim: int, + hidden_n_layers: int = 2, + hidden_n_units_first: int = 512, + hidden_decrease_rate: float = 0.5, + dropout: float | None = None, + activation: torch.nn.Module | None = torch.nn.ReLU(), + bias: bool = True, + num_epochs: int = 100, + batch_size: int = 256, + learning_rate: float = 1.0e-4, + early_stopping_use: bool = True, + max_num_epochs: int = 1000, + early_stopping_split: float = 0.2, + early_stopping_patience: int = 50, + early_stopping_delta: float = 0.001, + retrain: bool = True, + device: str | None = None, + random_state: int = 42, + criterion: torch.nn.Module = torch.nn.MSELoss(), + optimizer: Callable = torch.optim.Adam, + ): + if early_stopping_use and early_stopping_patience >= max_num_epochs: + raise ValueError("early_stopping_patience must be less than max_num_epochs") + + super().__init__() + + self.random_state = random_state + seed_everything(self.random_state) + self.repr_dim = repr_dim + self.hidden_n_layers = hidden_n_layers + self.hidden_n_units_first = hidden_n_units_first + self.hidden_decrease_rate = hidden_decrease_rate + self.hidden = self._convert_hidden_config() + self.dropout = dropout + self.activation = activation + self.bias = bias + self.num_epochs = max_num_epochs if early_stopping_use else num_epochs + self.batch_size = batch_size + self.learning_rate = learning_rate + self.early_stopping_use = early_stopping_use + self.early_stopping_split = early_stopping_split + self.early_stopping_patience = early_stopping_patience + self.early_stopping_delta = early_stopping_delta + self.device = get_device(device) + self.criterion = criterion + self.optimizer = optimizer + self.retrain = retrain + + # Attributes initialized during fit + self.in_features = 0 + self.feature_names_in_: list[str] | None = None + self.train_loss: list[float] + self.eval_loss: list[float] + self.early_stopping_epoch: int + self.encoder_early_stopping: torch.nn.Module | None = None + self.decoder_early_stopping: torch.nn.Module | None = None + self.encoder: torch.nn.Module + self.decoder: torch.nn.Module + self.early_stopping_best: float + + def _validate_feature_names(self, X: pd.DataFrame) -> None: + """Validate that feature names match those seen during fit. + + Parameters + ---------- + X : pd.DataFrame + Data to validate. + + Raises + ------ + ValueError + If feature names don't match those seen during fit. + """ + if self.feature_names_in_ is None: + raise ValueError("This AutoEncoder instance is not fitted yet. Call 'fit' before using this method.") + + X_feature_names = X.columns.tolist() + if X_feature_names != self.feature_names_in_: + raise ValueError( + f"The feature names should match those that were passed during fit.\n" + f"Feature names seen during fit: {self.feature_names_in_}\n" + f"Feature names seen now: {X_feature_names}" + ) + + def _convert_hidden_config(self) -> list[int]: + """Convert hidden layer config to list of layer sizes. + + Converts from the 3 hidden layer configs (n_layers, n_units_first, and decrease_rate) to the traditional hidden + list containing the number of nodes in each hidden layer. + + Returns + ------- + list[int] + List of hidden layer sizes. + """ + hidden: list[int] = [] + + if self.hidden_n_layers == 0: + return hidden + + hidden.append(self.hidden_n_units_first) + for i in range(1, self.hidden_n_layers): + hidden.append(int(hidden[i - 1] * self.hidden_decrease_rate)) + + return hidden + + def _init_models(self) -> None: + """Initialize the encoder/decoder neural networks. + + This method is separate from __init__ because it depends on the number of features in the training data X (the + shape of the first encoder layer and last decoder layer). This is called at each new fit() call. + """ + encoder_output_sizes = [*self.hidden, self.repr_dim] + decoder_output_sizes = self.hidden[::-1] if len(self.hidden) > 0 else [] + decoder_output_sizes.append(self.in_features) + + in_features_layer = self.in_features + + encoder_layers = [] + for i, size_of_layer_i in enumerate(encoder_output_sizes): + layer_args: list[torch.nn.Module] = [ + torch.nn.Linear(in_features=in_features_layer, out_features=size_of_layer_i, bias=self.bias) + ] + in_features_layer = size_of_layer_i + + # Don't add activation/dropout on the last layer (bottleneck) + if (self.activation is not None) and (i + 1 != len(encoder_output_sizes)): + layer_args.append(self.activation) + + if (self.dropout is not None) and (i + 1 != len(encoder_output_sizes)): + layer_args.append(torch.nn.Dropout(self.dropout)) + + encoder_layers.append(torch.nn.Sequential(*layer_args)) + + decoder_layers = [] + for i, size_of_layer_i in enumerate(decoder_output_sizes): + layer_args = [torch.nn.Linear(in_features=in_features_layer, out_features=size_of_layer_i, bias=self.bias)] + in_features_layer = size_of_layer_i + + # Don't add activation/dropout on the last layer (output) + if (self.activation is not None) and (i + 1 != len(decoder_output_sizes)): + layer_args.append(self.activation) + + if (self.dropout is not None) and (i + 1 != len(decoder_output_sizes)): + layer_args.append(torch.nn.Dropout(self.dropout)) + + decoder_layers.append(torch.nn.Sequential(*layer_args)) + + self.encoder = torch.nn.Sequential(*encoder_layers) + self.decoder = torch.nn.Sequential(*decoder_layers) + + self.encoder.to(self.device) + self.decoder.to(self.device) + + def forward(self, x: torch.Tensor, **kwargs: Any) -> torch.Tensor: + """Compute the encoding and decoding of the input x. + + Parameters + ---------- + x : torch.Tensor + Input tensor. + + Returns + ------- + torch.Tensor + Reconstructed output. + """ + return self.decoder(self.encoder(x)) + + def fit( + self, + X: pd.DataFrame, + metrics_suffix: str | None = None, + **kwargs: Any, + ) -> "AutoEncoder": + """Fit the autoencoder model to the training data. + + Parameters + ---------- + X : pd.DataFrame + Training data. + metrics_suffix : str | None + Suffix for logging metrics (e.g., repeat/split information). + + Returns + ------- + AutoEncoder + The fitted autoencoder instance. + """ + # Avoid printing "None" in logs + metrics_suffix = f" {metrics_suffix}" if metrics_suffix else "" + + # Reset loss tracking + self.train_loss, self.eval_loss = [], [] + + # Store feature names for validation during transform + if self.feature_names_in_ is None: + self.feature_names_in_ = X.columns.tolist() + + if self.early_stopping_use: + X_full = X.copy() + X, X_val = train_test_split(X, test_size=self.early_stopping_split, random_state=self.random_state) + + dataset = OmicsDataset(X.values) + dataloader = DataLoader(dataset, batch_size=self.batch_size, shuffle=True) + + sample = next(iter(dataloader)) + self.in_features = sample.shape[1] + self._init_models() + + optimizer = self.optimizer(self.parameters(), lr=self.learning_rate) + early_stopping_best, early_stopping_patience_count = 0.0, 0 + disable_bar = True + pbar = tqdm(range(self.num_epochs), total=self.num_epochs, disable=disable_bar) + + for epoch in pbar: + # Set to train mode (because evaluate() calls eval()) + self.train() + train_losses = [] + + for data_batch in dataloader: + data_batch = data_batch.to(self.device) + + data_batch_reconstructed = self.forward(data_batch) + loss = self.criterion(data_batch_reconstructed, data_batch) + + optimizer.zero_grad() + loss.backward() + optimizer.step() + + train_loss = loss.detach().cpu().numpy() + train_losses.append(train_loss) + + self.train_loss.append(np.mean(train_losses)) + pbar.set_description(f"train loss: {np.round(self.train_loss[-1], 4)!s}") + + if self.early_stopping_use: + with torch.no_grad(): + # Update the eval_loss at each epoch + self.evaluate(X_val) + + early_stopping_best = _initialize_early_stopping(self.eval_loss, early_stopping_best) + + (early_stopping_best, early_stopping_patience_count) = _update_early_stopping( + self.eval_loss, early_stopping_best, self.early_stopping_delta, early_stopping_patience_count + ) + + if early_stopping_patience_count > self.early_stopping_patience: + logger.info(f"AE training finished by early stopping at epoch {epoch + 1}") + self.early_stopping_epoch = epoch + 1 + break + + else: # No break occurred - finished all epochs + logger.info(f"AE training finished with the max epoch number: {epoch + 1}") + self.early_stopping_epoch = self.num_epochs + + if self.early_stopping_use: + self.early_stopping_use = False + self.num_epochs = self.early_stopping_epoch - self.early_stopping_patience + self.early_stopping_best = early_stopping_best + # Retrain on full data if requested + if self.retrain: + self.fit(X_full, metrics_suffix=metrics_suffix + " retrain") + + return self + + def evaluate(self, X: pd.DataFrame) -> "AutoEncoder": + """Evaluate the model performance on validation data. + + Updates self.eval_loss by computing reconstruction loss on X. + + Parameters + ---------- + X : pd.DataFrame + Validation data. + + Returns + ------- + AutoEncoder + The instance with eval_loss updated. + """ + self._validate_feature_names(X) + dataset = OmicsDataset(X.values) + dataloader = DataLoader(dataset, batch_size=self.batch_size, shuffle=False) + + self.eval() + eval_losses = [] + + for data_batch in dataloader: + data_batch = data_batch.to(self.device) + data_batch_reconstructed = self.forward(data_batch, eval_mode=True) + loss = self.criterion(data_batch_reconstructed, data_batch) + eval_losses.append(loss.detach().cpu().numpy()) + + self.eval_loss.append(np.mean(eval_losses)) + + return self + + def transform(self, X: pd.DataFrame) -> np.ndarray: + """Encode the data using the fitted model. + + Parameters + ---------- + X : pd.DataFrame + Data to transform. + + Returns + ------- + np.ndarray + Transformed data in the learned representation space. + """ + self._validate_feature_names(X) + dataset = OmicsDataset(X.values) + dataloader = DataLoader(dataset, batch_size=self.batch_size, shuffle=False) + + features_list = [] + self.eval() + + with torch.no_grad(): + for data_batch in dataloader: + data_batch = data_batch.to(self.device) + data_batch_encoded = self.encoder(data_batch) + features_list.append(data_batch_encoded.detach().cpu()) + + features = torch.cat(features_list, dim=0).numpy() + + return features diff --git a/src/leap/representation_models/masked_auto_encoder.py b/src/leap/representation_models/masked_auto_encoder.py new file mode 100644 index 0000000..5945cd1 --- /dev/null +++ b/src/leap/representation_models/masked_auto_encoder.py @@ -0,0 +1,183 @@ +"""Masked Autoencoder implementation for self-supervised pre-training. + +This module implements the Masked Autoencoder as described in: +"VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain" (2020) +""" + +from collections import defaultdict +from typing import Any, Literal + +import torch + +from .auto_encoder import AutoEncoder + + +class MaskedAutoencoder(AutoEncoder): + """Masked Autoencoder for self-supervised pre-training. + + Inherits from AutoEncoder and adds: + 1. Pretraining with masking/corruption + 2. Option to train with reconstruction and mask prediction loss + + Parameters + ---------- + beta : float + Noise level (used when corruption_method is set to "noise"). + corruption_proba : float + Masking/corruption probability. + corruption_method : Literal["classic", "vime", "noise", "full_noise"] + Type of corruption to use: + - "classic": Regular masking (zeros) + - "vime": Permutation-based corruption + - "noise": Additive Gaussian noise + - "full_noise": Full Gaussian noise replacement + data_augmentation : bool + Whether to add Gaussian noise to the input data as augmentation. + da_noise_std : float + Standard deviation of the Gaussian noise added to the input data when data_augmentation is True. + + + Attributes + ---------- + losses : dict[str, list[float]] + Dictionary storing different types of losses during training. + metrics : dict[str, list[float]] + Dictionary storing different metrics during training. + + Raises + ------ + ValueError + If corruption_method is not one of the allowed values. + If data_augmentation is True but corruption_method is not compatible. + + Examples + -------- + >>> import pandas as pd + >>> import numpy as np + >>> from leap.representation_models import MaskedAutoencoder + >>> + >>> X = pd.DataFrame(np.random.randn(100, 50)) + >>> mae = MaskedAutoencoder(repr_dim=10, corruption_proba=0.3, corruption_method="vime") + >>> X_transformed = mae.fit(X) + >>> X_transformed = mae.transform(X) + """ + + def __init__( + self, + beta: float = 0.1, + corruption_proba: float = 0.3, + corruption_method: Literal["classic", "vime", "noise", "full_noise"] = "classic", + data_augmentation: bool = False, + da_noise_std: float = 0.01, + **kwargs: Any, + ): + super().__init__(**kwargs) + + # Mask configuration + self.corruption_proba = corruption_proba + self.beta = beta + self.losses: dict[str, list[float]] = defaultdict(list) + self.metrics: dict[str, list[float]] = defaultdict(list) + + # Validate corruption method + valid_methods = ["classic", "vime", "noise", "full_noise"] + if corruption_method not in valid_methods: + raise ValueError(f"corruption_method must be one of {valid_methods}, got '{corruption_method}'") + self.corruption_method = corruption_method + + # Data augmentation configuration + self.data_augmentation = data_augmentation + self.da_noise_std = da_noise_std + + def mask_generator(self, x: torch.Tensor) -> torch.Tensor: + """Generate random mask vector. + + Parameters + ---------- + x : torch.Tensor + Input tensor. + + Returns + ------- + torch.Tensor + Binary mask tensor with corruption_proba chance of 1. + """ + tensor_p = torch.ones_like(x) * self.corruption_proba + mask = torch.bernoulli(tensor_p) + return mask + + def pretext_generator(self, mask: torch.Tensor, x: torch.Tensor) -> torch.Tensor: + """Generate corrupted samples according to the corruption method. + + Parameters + ---------- + mask : torch.Tensor + Binary mask indicating which elements to corrupt. + x : torch.Tensor + Original input tensor. + + Returns + ------- + torch.Tensor + Corrupted input tensor. + """ + n, dim = x.shape + + if self.corruption_method == "vime": + # Permutation-based corruption + perm = torch.randperm(x.size(0), device=x.device) + x_bar = x[perm] + elif self.corruption_method == "noise": + # Additive Gaussian noise + x_noise = torch.randn_like(x) + x_bar = x + self.beta * x_noise + elif self.corruption_method == "full_noise": + # Full Gaussian noise replacement + x_bar = torch.randn_like(x) + else: # "classic" + # Zero masking + x_bar = torch.zeros([n, dim], device=self.device) + + # Apply mask: keep original where mask=0, corrupt where mask=1 + x_tilde = x * (1 - mask) + x_bar * mask + return x_tilde + + def forward(self, x: torch.Tensor, eval_mode: bool = False, **kwargs: Any) -> torch.Tensor: + """Forward pass through the masked autoencoder. + + Parameters + ---------- + x : torch.Tensor + Input tensor. + eval_mode : bool + If True, skip masking (standard autoencoder reconstruction). + + Returns + ------- + torch.Tensor + Reconstructed/decoded tensor. + + Raises + ------ + ValueError + If data_augmentation is True but corruption_method is not compatible. + """ + if eval_mode: + # Standard reconstruction without masking + return self.decoder(self.encoder(x)) + + # Apply data augmentation if enabled + if self.data_augmentation: + # Validate compatible corruption methods + if self.corruption_method not in ["classic", "vime", "full_noise"]: + raise ValueError( + "data_augmentation can only be used with corruption_method 'classic', 'vime', or 'full_noise'" + ) + x = x + self.da_noise_std * torch.randn_like(x) + + # Generate mask and corrupted input + mask = self.mask_generator(x) + x_tilde = self.pretext_generator(mask, x) + + # Encode and decode + return self.decoder(self.encoder(x_tilde)) diff --git a/src/leap/representation_models/pca.py b/src/leap/representation_models/pca.py new file mode 100644 index 0000000..80dc5ba --- /dev/null +++ b/src/leap/representation_models/pca.py @@ -0,0 +1,81 @@ +"""Dimension reduction method based on principal component analysis.""" + +from typing import Any + +import numpy as np +import pandas as pd +import sklearn.decomposition + +from . import RepresentationModelBase + + +class PCA(sklearn.decomposition.PCA, RepresentationModelBase): + """Principal Component Analysis for dimensionality reduction. + + This class extends sklearn's PCA with a consistent interface for LEAP, including support for pandas DataFrames. + + Parameters + ---------- + repr_dim : int + Number of dimensions for the dimension reduction (number of components). + random_state : int + Seed for the random number generator. + + Attributes + ---------- + repr_dim : int + Number of principal components. + components_ : np.ndarray + Principal axes in feature space. + + Examples + -------- + >>> import pandas as pd + >>> import numpy as np + >>> from leap.representation_models import PCA + >>> + >>> X = pd.DataFrame(np.random.randn(100, 50)) + >>> pca = PCA(repr_dim=10) + >>> pca.fit(X) + >>> X_transformed = pca.transform(X) + >>> print(X_transformed.shape) + (100, 10) + """ + + def __init__(self, repr_dim: int, random_state: int = 42): + super().__init__(n_components=repr_dim, random_state=random_state) + self.repr_dim = repr_dim + + def fit(self, X: pd.DataFrame, **kwargs: Any) -> "PCA": + """Fit the PCA model with X. + + Parameters + ---------- + X : pd.DataFrame + Training matrix of shape (n_samples, n_features). + + Returns + ------- + PCA + The fitted instance. + """ + # Convert DataFrame to numpy array for sklearn + # super() calls sklearn.decomposition.PCA.fit() due to MRO + super().fit(X) # type: ignore[safe-super] + return self + + def transform(self, X: pd.DataFrame) -> np.ndarray: + """Apply dimensionality reduction to X. + + Parameters + ---------- + X : pd.DataFrame + Data to transform, shape (n_samples, n_features). + + Returns + ------- + np.ndarray + Transformed values, shape (n_samples, n_components). + """ + # super() calls sklearn.decomposition.PCA.transform() due to MRO + return super().transform(X) # type: ignore[safe-super] diff --git a/src/leap/representation_models/utils.py b/src/leap/representation_models/utils.py new file mode 100644 index 0000000..f66c268 --- /dev/null +++ b/src/leap/representation_models/utils.py @@ -0,0 +1,96 @@ +"""Utility functions for representation models.""" + +import numpy as np +import torch + + +class OmicsDataset(torch.utils.data.Dataset): + """OmicsDataset Dataset Class for PyTorch models. + + Parameters + ---------- + X : np.ndarray + Features. + y : np.ndarray | None + Labels, by default None. + """ + + def __init__(self, X: np.ndarray, y: np.ndarray | None = None): + self.X = X + self.y = y + + def __len__(self) -> int: + """Return the number of samples.""" + return len(self.X) + + def __getitem__(self, item: int) -> tuple[torch.Tensor, torch.Tensor] | torch.Tensor: + """Get a single sample from the dataset.""" + features = torch.from_numpy(self.X[item].astype(np.float32)) + if self.y is not None: + label = torch.Tensor([self.y[item]]).float() + return features, label + return features + + +def _initialize_early_stopping(eval_loss: list[float], early_stopping_best: float) -> float: + """Initialize the current best loss at the first epoch. + + Only useful for the AE, as other models' metrics can be initialized to their worst value (e.g., 0). + + Parameters + ---------- + eval_loss : list[float] + The list of loss at every epoch. + early_stopping_best : float + The best validation performance so far. + + Returns + ------- + float + Either the initialized early_stopping_best or the current one. + """ + if len(eval_loss) == 1: # First epoch + early_stopping_best = eval_loss[0] + return early_stopping_best + + +def _update_early_stopping( + eval_list: list[float], + early_stopping_best: float, + early_stopping_delta: float, + early_stopping_patience_count: int, + use_metric: bool = False, +) -> tuple[float, int]: + """Update the best loss/metric value epoch by epoch, along with the patience count. + + If we use a metric (higher is better), we want it to increase. If we use the loss (lower is better), we want it to + decrease. + + Parameters + ---------- + eval_list : list[float] + Either the metric or the loss at each epoch. + early_stopping_best : float + The current best performance on the eval set. + early_stopping_delta : float + The threshold for which we consider the model hasn't improved enough. + early_stopping_patience_count : int + The current number of epochs the model hasn't improved enough. + use_metric : bool + Whether a metric is used (True) or the loss (False). + + Returns + ------- + tuple[float, int] + The updated early_stopping_best and early_stopping_patience_count. + """ + if use_metric and (eval_list[-1] > early_stopping_best + early_stopping_delta): + early_stopping_best = eval_list[-1] + early_stopping_patience_count = 0 + elif not use_metric and (eval_list[-1] < early_stopping_best - early_stopping_delta): + early_stopping_best = eval_list[-1] + early_stopping_patience_count = 0 + else: + early_stopping_patience_count += 1 + + return (early_stopping_best, early_stopping_patience_count) diff --git a/src/leap/trainer/__init__.py b/src/leap/trainer/__init__.py new file mode 100644 index 0000000..7218da8 --- /dev/null +++ b/src/leap/trainer/__init__.py @@ -0,0 +1 @@ +"""Trainer for LEAP.""" diff --git a/src/leap/trainer/perturbation_model_trainer.py b/src/leap/trainer/perturbation_model_trainer.py new file mode 100644 index 0000000..5925b8e --- /dev/null +++ b/src/leap/trainer/perturbation_model_trainer.py @@ -0,0 +1,1067 @@ +"""Trainer for LEAP.""" + +import copy +import os +import subprocess +import time +from pathlib import Path +from typing import Any, TypedDict, cast + +import numpy as np +import pandas as pd +import psutil +import torch +from loguru import logger +from ml_collections import config_dict + +from leap.data.preclinical_dataset import PreclinicalDataset +from leap.metrics.regression_metrics import RegressionMetricType, performance_metric_wrapper +from leap.pipelines.perturbation_pipeline import PerturbationPipeline +from leap.utils.config_utils import instantiate +from leap.utils.io import save_pickle + + +class SplitPairIds(TypedDict): + """Dictionary of sample x perturbation pairs in each split.""" + + training_ids: list[tuple[Any, Any]] + test_ids: list[tuple[Any, Any]] + + +class SplitIds(TypedDict): + """Dictionary of sample ids in each split.""" + + training_ids: list + test_ids: list + + +def check_list_pair(value: Any) -> list[tuple[Any, Any]]: + """Check that the input is a list of pairs.""" + if not isinstance(value, list): + raise ValueError(f"Expected a list, got {type(value)}") + if not all(isinstance(pair, tuple) for pair in value): + raise ValueError(f"Expected a list of pairs, got {value}") + if not all(len(pair) == 2 for pair in value): + raise ValueError(f"Expected a list of pairs, got {value}") + return cast(list[tuple[Any, Any]], value) + + +class PerturbationModelTrainer: + """Trainer for perturbation prediction models. + + This class manages the complete pipeline for training and evaluating perturbation prediction models, including data + splitting, model training, prediction, and performance evaluation. + + Parameters + ---------- + source_domain_data : config_dict.ConfigDict + The source domain data configuration. + data_split : config_dict.ConfigDict + The split configuration for source domain data. + model : config_dict.ConfigDict + The model configuration. + target_domain_data : config_dict.ConfigDict | None + The target domain data configuration, optional. + """ + + def __init__( + self, + source_domain_data: config_dict.ConfigDict, + data_split: config_dict.ConfigDict, + model: config_dict.ConfigDict, + target_domain_data: config_dict.ConfigDict | None = None, + ): + # Store configurations + self.config_source_domain_data = source_domain_data + self.config_data_split = data_split + self.config_model = model + self.config_target_domain_data = target_domain_data + self._output_path: Path | None = None + + # Initialize other attributes + self._data: PreclinicalDataset | None = None + self.split_pair_ids: dict[str, SplitPairIds] = {} + self.trained_model: dict[str, PerturbationPipeline] = {} + self.test_predicted_labels: dict[Any, pd.DataFrame] = {} + self.test_performance: dict[str, dict[str, dict[Any, dict[str, float]]]] = {} + self.test_performance_aggregated: dict[str, dict[str, dict[str, str]]] = {} + self.run_time: str = "0" + + # Store n cpus, n gpus and ram to keep context of run time estimation + try: + self.n_cpus = subprocess.run("nproc", capture_output=True, text=True, check=True).stdout.strip() + except FileNotFoundError: + self.n_cpus = subprocess.run( + ["sysctl", "-n", "hw.logicalcpu"], capture_output=True, text=True, check=True + ).stdout.strip() + self.n_gpus = torch.cuda.device_count() + try: + self.ram = subprocess.run( + "free -h | grep Mem | tr -s ' ' | cut -d ' ' -f 2", + shell=True, + capture_output=True, + text=True, + check=True, + ).stdout.strip() + except (FileNotFoundError, subprocess.CalledProcessError): + # macOS doesn't have 'free' command + self.ram = subprocess.run( + ["sysctl", "-n", "hw.memsize"], capture_output=True, text=True, check=True + ).stdout.strip() + # Convert bytes to GB for consistency with Linux output + self.ram = f"{int(self.ram) / (1024**3):.1f}G" + + @property + def output_path(self) -> Path: + """Return the output path.""" + if self._output_path is None: + raise ValueError("Output path is not set") + return self._output_path + + @output_path.setter + def output_path(self, output_path: Path) -> None: + """Set the output path.""" + self._output_path = output_path + + @property + def data(self) -> PreclinicalDataset: + """Return the source and target domain data. + + Note: This is a property to ensure data is loaded before access. + """ + if self._data is None: + raise ValueError("Data is not loaded") + return self._data + + @data.setter + def data(self, data: PreclinicalDataset) -> None: + """Set the source and target domain data.""" + self._data = data + + def __setstate__(self, state: dict[str, Any]) -> None: + if "data" in state: + state["_data"] = state.pop("data") + if "output_path" in state: + state["_output_path"] = state.pop("output_path") + self.__dict__.update(state) + + def __str__(self) -> str: + description = """Class attributes: + + Configurations: + config_source_domain_data: source domain data configuration + config_data_split: split configuration in source domain data + config_model: model configuration + config_target_domain_data: target domain data configuration + + Results: + data: source and target domain data + split_pair_ids: dictionary of sample x perturbation pairs in each split + trained_model: trained prediction model + test_predicted_labels: predictions on the test set(s) + test_performance: performance on the test set(s) + test_performance_aggregated: aggregated performance on the test set(s) + run_time: time to run the pipeline (in seconds) + n_cpus: total number of cpus where the trainer is instantiated + n_gpus: total number of gpus where the trainer is instantiated + ram: total ram where the trainer is instantiated + """ + return description + + def run( + self, + output_path: str = "", + performance_per_perturbation: bool | list[bool] | None = None, + metric: RegressionMetricType | list[RegressionMetricType] | None = None, + should_load_data: bool = True, + save_test_predicted_labels: bool = False, + save_trainer: bool = True, + start_split_n: int = -1, + n_splits: int = -1, + ) -> None: + """Run the pipeline. + + There are 5 steps: + 1. Load the data (optional). + 2. Split the data into training and test sets. + 3. Train the model (including hyper-parameter tuning in CV). + 4. Predict labels in the test set(s). + 5. Evaluate prediction performance in the test set(s). + 6. Save the results if output_path is provided. The prediction performances in the test set(s) are saved in a + .pkl file. The predicted labels and the entire trainer can also be saved in separate .pkl files. + + Parameters + ---------- + output_path : str, optional + Path to the output folder where the results are saved. This path must end with experiment_name / date_time. + The final instance of the class, predicted labels and performances are pickled and saved in the specified + folder. The logs are also saved in the same folder. The results are not saved if the output_path is "". + performance_per_perturbation : bool | list[bool] | None + Whether to evaluate performance per perturbation (True) or over all perturbations (False). By default, both + metrics are computed. + metric : RegressionMetricType | list[RegressionMetricType] | None + The metric(s) to use for evaluation. Possible values are: "spearman", "pearson", "r2", "mse" and "mae". By + default, all metrics are computed. + should_load_data : bool, optional + Whether to load the data. The data can be loaded separately in a script for more complex cases (for + instance, for the DDT benchmark), by default True. + save_test_predicted_labels : bool, optional + Whether to save the predicted labels in the test set(s), by default False. + save_trainer : bool, optional + Whether to save the final instance of the class, by default True. + start_split_n : int, optional + The split number to start from, by default -1 to start from the first split. + n_splits : int, optional + The number of splits to keep, by default -1 to run all splits. + """ + # Define arguments + if performance_per_perturbation is None: + performance_per_perturbation = [True, False] + if metric is None: + metric = ["spearman", "pearson", "r2", "mse", "mae"] + + # Prepare output paths + if output_path != "": + # Create a folder with the following structure: + # output_path/ + # experiment_name/ + # date_time/ + # experiment_name_perf.pkl + # experiment_name_pred.pkl + # experiment_name_trainer.pkl + # saved_ensembles/ + # split_id/ + # model_perturbation_A_id.pkl + # model_perturbation_B_id.pkl + # ... + + # Extract the experiment_name from the output path provided + experiment_name = Path(output_path).parts[-2] + self.output_path = Path(output_path) + + # Define the file names + log_file = self.output_path.joinpath(f"{experiment_name}.log") + data_summary_file = self.output_path.joinpath(f"{experiment_name}_data_summary.csv") + perf_pkl_file = self.output_path.joinpath(f"{experiment_name}_perf.pkl") + pred_pkl_file = self.output_path.joinpath(f"{experiment_name}_pred.pkl") + trainer_pkl_file = self.output_path.joinpath(f"{experiment_name}_trainer.pkl") + + # Start a new log file + logger.add(log_file) + logger.info(f"Starting pipeline with output path: {perf_pkl_file}") + + # Load the data + if should_load_data: + self.data = self.load_data() + + # Log and save data summary + self.log_data_summary(data=self.data) + if output_path != "": + self.save_data_summary(data=self.data, output_path=data_summary_file) + + # Split the data into training and test sets + self.split_pair_ids = self.split_training_test(data=self.data) + + # if provided, keep only n_splits splits starting from start_split_n + if n_splits > 0: + logger.info(f"Keeping only {n_splits} splits") + if start_split_n < 0: + logger.warning("As start_split_n was left unspecified, it was set to 0.") + start_split_n = 0 + self.split_pair_ids = self._keep_n_splits( + split_pair_ids=self.split_pair_ids, start_split_n=start_split_n, n_splits=n_splits + ) + + # Train models for each of the training sets. + self.train(data=self.data, split_pair_ids=self.split_pair_ids) + + # Evaluate performances in the test sets + self.test_predicted_labels = self.predict_test(data=self.data, split_pair_ids=self.split_pair_ids) + self.test_performance, self.test_performance_aggregated = self.evaluate( + test_true_labels=self.get_test_true_labels(data=self.data, split_pair_ids=self.split_pair_ids), + test_predicted_labels=self.test_predicted_labels, + performance_per_perturbation=performance_per_perturbation, + metric=metric, + ) + + # Save the predicted values and performances + if output_path != "": + self.save_attribute("test_performance", perf_pkl_file) + if save_test_predicted_labels: + self.save_attribute("test_predicted_labels", pred_pkl_file) + + # Save the trainer + if output_path != "" and save_trainer: + self.save_trainer(trainer_pkl_file) + + def _keep_n_splits( + self, split_pair_ids: dict[str, SplitPairIds], start_split_n: int, n_splits: int + ) -> dict[str, SplitPairIds]: + """Keep only n_splits splits starting from start_split_n.""" + # Check that there are at least n_splits + start_split_n splits + if start_split_n + n_splits > len(split_pair_ids): + raise ValueError(f"Not enough splits to keep {n_splits} starting from {start_split_n}") + # Check that start_split_n is valid + if start_split_n < 0 or start_split_n >= len(split_pair_ids): + raise ValueError(f"Invalid start split number: {start_split_n}") + logger.info(f"Keeping only {n_splits} splits starting from {start_split_n}") + return dict(list(split_pair_ids.items())[start_split_n : start_split_n + n_splits]) + + def load_data(self) -> PreclinicalDataset: + """Load the data based on the source and target domain configurations. + + Returns + ------- + PreclinicalDataset + The full dataset, including source and target domains (if available). + """ + dataset: PreclinicalDataset = instantiate(self.config_source_domain_data) + dataset.df_sample_metadata["domain"] = "source" + dataset.stack_dataframes() + if self.config_target_domain_data is not None: + # Load target domain data if available + dataset_target: PreclinicalDataset = instantiate(self.config_target_domain_data) + dataset_target.df_sample_metadata["domain"] = "target" + dataset_target.stack_dataframes() + + # Keep common perturbations between source and target domains + perturbation_names = dataset.df_labels.columns.intersection(dataset_target.df_labels.columns).tolist() + dataset.keep_perturbations(perturbation_names) + dataset_target.keep_perturbations(perturbation_names) + + # Keep common columns between source and target domains + columns_name = dataset.df_rnaseq.columns.intersection(dataset_target.df_rnaseq.columns).tolist() + if len(columns_name) < len(dataset.df_rnaseq.columns): + logger.warning( + f"Dropping {len(dataset.df_rnaseq.columns) - len(columns_name)} genes that" + " are not common between source and target domains." + ) + dataset.df_rnaseq = dataset.df_rnaseq[columns_name] + dataset_target.df_rnaseq = dataset_target.df_rnaseq[columns_name] + + # Keep only samples with at least one label for those common perturbations + dataset_target.df_labels.dropna(how="all", inplace=True) + dataset_target.align_sample_data() + dataset.df_labels.dropna(how="all", inplace=True) + dataset.align_sample_data() + # Concatenate source and target domain data + dataset.merge(dataset_target) + dataset._sort_rows_and_columns() + + return dataset + + def log_data_summary(self, data: PreclinicalDataset) -> None: + """Log number of genes, perturbations and samples in the data. + + Parameters + ---------- + data : PreclinicalDataset + The full dataset, including source and target domains (if available). + """ + message = ( + f"Data summary: " + f"{len(data.df_labels.columns)} perturbations, " + f"{len(data.df_labels)} samples, " + f"{data.df_labels.count().sum()} unique pairs, " + f"{len(data.df_rnaseq.columns)} genes" + ) + logger.info(message) + + def save_data_summary(self, data: PreclinicalDataset, output_path: Path) -> None: + """Save the data summary.""" + # Initialise dictionary to store counts + count_dict = [] + index = [] + + # Count the number of genes, perturbations and samples + count_dict.append(len(data.df_rnaseq.columns)) + index.append("Number of genes") + count_dict.append(len(data.df_labels)) + index.append("Number of samples") + count_dict.append(len(data.df_labels.columns)) + index.append("Number of perturbations") + count_dict.append(data.df_labels.count().sum()) + index.append("Number of sample x perturbation pairs") + + # Save the data summary + pd.DataFrame( + count_dict, + index=index, + ).transpose().to_csv( + str(output_path), + index=True, + header=True, + ) + + def split_training_test(self, data: PreclinicalDataset) -> dict[str, SplitPairIds]: + """Create training and test splits. + + The indices of samples x perturbation pairs in: + - the training set: source domain samples only + - the test set: target domain samples only (if available), otherwise source domain samples only + + Parameters + ---------- + data : PreclinicalDataset + The full dataset, including source and target domains (if available). + + Returns + ------- + dict[str, SplitPairIds] + The dictionary of sample x perturbation pairs in each split. The keys are the split ids and values are typed + dictionaries with keys "training_ids" and "test_ids". + """ + # Initialise dictionary to store split ids + split_pair_ids: dict[str, SplitPairIds] = {} + + if "target" in data.df_sample_metadata["domain"].tolist(): + # If a target domain is provided: the full source domain data is used as training set and the target domain + # data is split into a test set. + + # Define training set as full source domain data + training_ids = check_list_pair( + data.df_sample_metadata_stacked.loc[ + data.df_sample_metadata_stacked["domain"] == "source" + ].index.to_list() + ) + + # Create splits of sample x perturbation pairs from target domain + split_data = instantiate(self.config_data_split) + split_ids_dict = split_data( + X_metadata=data.df_sample_metadata_stacked.loc[data.df_sample_metadata_stacked["domain"] == "target"] + ) + + # Store all split ids + for split in split_ids_dict: + split_pair_ids[split] = SplitPairIds( + training_ids=training_ids, + test_ids=check_list_pair(split_ids_dict[split]["test_ids"]), + ) + else: + # If only a source domain is provided: it is split into a training set and a test set. + # Create splits of sample x perturbation pairs from source domain + split_data = instantiate(self.config_data_split) + split_ids_dict = split_data( + X_metadata=data.df_sample_metadata_stacked.loc[data.df_sample_metadata_stacked["domain"] == "source"] + ) + + # Store all split ids + for split in split_ids_dict: + split_pair_ids[split] = SplitPairIds( + training_ids=check_list_pair(split_ids_dict[split]["training_ids"]), + test_ids=check_list_pair(split_ids_dict[split]["test_ids"]), + ) + + return split_pair_ids + + def extract_split_sample_ids(self, split_pair_ids: dict[str, SplitPairIds]) -> dict[Any, SplitIds]: + """Extract sample ids in each split. + + Parameters + ---------- + split_pair_ids : dict[str, SplitPairIds] + The dictionary of sample x perturbation pairs in each split. + + Returns + ------- + dict[Any, SplitIds] + Dictionary of sample ids in each split. + """ + return self._loop_over_ids(split_pair_ids, pair_id=0) + + def extract_split_perturbation_ids(self, split_pair_ids: dict[str, SplitPairIds]) -> dict[Any, SplitIds]: + """Extract perturbation ids in each split. + + Parameters + ---------- + split_pair_ids : dict[str, SplitPairIds] + The dictionary of sample x perturbation pairs in each split. + + Returns + ------- + dict[Any, SplitIds] + Dictionary of perturbation ids in each split. + """ + return self._loop_over_ids(split_pair_ids, pair_id=1) + + def _loop_over_ids(self, split_pair_ids: dict[str, SplitPairIds], pair_id: int = 0) -> dict[Any, SplitIds]: + """Loop over sample x perturbation pairs to extract sample/perturbation ids.""" + split_ids: dict[Any, SplitIds] = {} + for split, split_dict in split_pair_ids.items(): + split_ids[split] = SplitIds( + training_ids=self._get_pair_member(pair_list=split_dict["training_ids"], pair_id=pair_id), + test_ids=self._get_pair_member(pair_list=split_dict["test_ids"], pair_id=pair_id), + ) + + return split_ids + + @staticmethod + def _get_pair_member(pair_list: list[tuple[Any, Any]], pair_id: int) -> list: + if pair_id not in {0, 1}: + raise ValueError("pair_id must be 0 or 1") + return list(np.unique([pair[pair_id] for pair in pair_list])) + + def train(self, data: PreclinicalDataset, split_pair_ids: dict[str, SplitPairIds]) -> None: + """Train the model. + + Parameters + ---------- + data : PreclinicalDataset + The full dataset, including source and target domains (if available). + split_pair_ids : dict[str, SplitPairIds] + The dictionary of sample x perturbation pairs in each split. + """ + start_time = time.time() + # Initialise dictionary to store trained models and save first split name + for split, split_pair_id in split_pair_ids.items(): + n_splits = len(split_pair_ids) + logger.info(f"Training model, {split.replace('_', ' ')} over {n_splits}...") + + # Extract fingerprints + X_fgpt = data.df_fingerprints + + # Extract training data + X, y, X_metadata = self._get_training_data(data, split_pair_id) + + # The RegressionModel is instantiated from the config. + self.trained_model[split] = copy.deepcopy(instantiate(self.config_model)) + + # Add the ensembling output path to the model if provided + if self._output_path is not None: + ensembling_output_path = self.output_path.joinpath(f"saved_model_cv_ensembling_{split}") + self.trained_model[split].ensembling_output_path = ensembling_output_path + + # Fit the model on the training data + self.trained_model[split].fit(X=X, y=y.loc[X.index], X_fgpt=X_fgpt, X_metadata=X_metadata.loc[X.index]) + + # Log memory usage + process = psutil.Process(os.getpid()) + logger.info( + f"Memory usage at the end of {split.replace('_', ' ')}: {process.memory_info().rss / (1024**3):.2f} GB" + ) + + end_time = time.time() + self.run_time = f"{end_time - start_time:.0f}" + logger.info(f"Training time: {self.run_time} seconds") + + def _get_training_data( + self, data: PreclinicalDataset, current_split_pair_ids: SplitPairIds + ) -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]: + """Extract the training data for a given split. + + Parameters + ---------- + data : PreclinicalDataset + The full dataset, including source and target domains (if available). + current_split_pair_ids : SplitPairIds + The dictionary of sample x perturbation pairs in the current split. + + Returns + ------- + tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame] + The training input data (X), training label data (y), and training metadata (X_metadata). + """ + # Define sample x perturbation pairs in the training set + training_pair_ids = current_split_pair_ids["training_ids"] + + # Extract sample ids from the sample x perturbation pairs in the training set + training_sample_ids = self._get_pair_member(training_pair_ids, 0) + + # Define the training label data + # The labels are unstacked to go back to a dataframe where rows are samples and columns are perturbations. The + # training/test split is created here by replacing some of the sample x perturbation pairs by NaN. The NaN + # values in label data are filtered out internally when calling fit below. When using a grouping by sample in + # self.split_training_test, then this is equivalent to a training/test split by sample. However, this framework + # is more general as it allows for perturbation-specific training/test splits. This is needed for drug response + # as a given drug has not been tested on all samples (it ensures that the same training/test set proportions are + # used across all perturbations). + y = data.df_labels_stacked.loc[training_pair_ids]["label"].unstack() + # Extract RNASeq data + X = data.df_rnaseq.loc[training_sample_ids] + # Define the training metadata + X_metadata = data.df_sample_metadata.loc[training_sample_ids] + + return ( + X, + y, + X_metadata, + ) + + def get_test_true_labels( + self, data: PreclinicalDataset, split_pair_ids: dict[str, SplitPairIds] + ) -> dict[Any, pd.DataFrame]: + """Get labels in the test set(s). + + Parameters + ---------- + data : PreclinicalDataset + The full dataset, including source and target domains (if available). + split_pair_ids : dict[str, SplitPairIds] + The dictionary of sample x perturbation pairs in each split. + + Returns + ------- + dict[Any, pd.DataFrame] + Dictionary of labels in the test set(s). + """ + test_true_labels = {} + for split in split_pair_ids: + # Define the test label data + y_stacked_test = data.df_labels_stacked.loc[split_pair_ids[split]["test_ids"]]["label"] + y_test = y_stacked_test.unstack() + + # Store the test labels + test_true_labels[split] = y_test + + return test_true_labels + + def predict_test( + self, data: PreclinicalDataset, split_pair_ids: dict[str, SplitPairIds] + ) -> dict[Any, pd.DataFrame]: + """Predict labels in test data. + + Parameters + ---------- + data : PreclinicalDataset + The full dataset, including source and target domains (if available). + split_pair_ids : dict[str, SplitPairIds] + The dictionary of sample x perturbation pairs in each split. + + Returns + ------- + dict[Any, pd.DataFrame] + Dictionary of predicted labels in the test set(s). + + Raises + ------ + ValueError + If there is no test set to predict. + """ + # Predict labels if any of the test sets is not empty + if any(len(split_dict["test_ids"]) > 0 for split_dict in split_pair_ids.values()): + split_sample_ids = self.extract_split_sample_ids(split_pair_ids) + split_perturbation_ids = self.extract_split_perturbation_ids(split_pair_ids) + test_predicted_labels = {} + for split, split_dict in split_sample_ids.items(): + # Fingerprints are always loaded in the trainer but only used by some + # models + X_fgpt = data.df_fingerprints + X = data.df_rnaseq.loc[split_dict["test_ids"]] + test_predicted_labels[split] = self.trained_model[split].predict( + X=X, + X_metadata=data.df_sample_metadata.loc[split_dict["test_ids"]], + X_fgpt=X_fgpt, + # Predict only for perturbations in the test set + list_of_perturbations=split_perturbation_ids[split]["test_ids"], + ) + + return test_predicted_labels + raise ValueError("No test set to predict") + + def evaluate( + self, + test_true_labels: dict[Any, pd.DataFrame], + test_predicted_labels: dict[Any, pd.DataFrame], + performance_per_perturbation: bool | list[bool] | None = None, + metric: RegressionMetricType | list[RegressionMetricType] | None = None, + ) -> tuple[ + dict[str, dict[str, dict[Any, dict[str, float]]]], + dict[str, dict[str, dict[str, str]]], + ]: + """Evaluate prediction performance in test data. + + Parameters + ---------- + test_true_labels : dict[Any, pd.DataFrame] + Dictionary of true labels in the test set(s). + test_predicted_labels : dict[Any, pd.DataFrame] + Dictionary of predicted labels in the test set(s). + performance_per_perturbation : bool | list[bool] | None + Whether to evaluate performance per perturbation (True) or over all perturbations (False). By default, both + per perturbation and overall performances are calculated. + metric : RegressionMetricType | list[RegressionMetricType] | None + The metric(s) to use for evaluation. Possible values are: "spearman", "pearson", "r2", "mse" and "mae". By + default, all possible values are calculated. + + Returns + ------- + tuple[dict[str, dict[str, dict[Any, dict[str, float]]]], dict[str, dict[str, dict[str, str]]]] + The dictionary of performances per split, per metric and per perturbation + (mapping perturbation, metric and split) + (if applicable) and the dictionary of aggregated performances over splits + and labels. + """ + logger.info("Evaluating prediction performances...") + + # Define arguments + if performance_per_perturbation is None: + performance_per_perturbation = [True, False] + if metric is None: + metric = ["spearman", "pearson", "r2", "mse", "mae"] + # Calculate the performances + test_performance = self.compute_performances( + test_true_labels=test_true_labels, + test_predicted_labels=test_predicted_labels, + performance_per_perturbation=performance_per_perturbation, + metric=metric, + ) + + # Aggregate the performances over splits and labels + + # Since format_numbers is true, the values are formatted to 4 decimal places + test_performance_aggregated = cast( + dict[str, dict[str, dict[str, str]]], + self.aggregate_performances(test_performance=test_performance, format_numbers=True), + ) + # Log average overall and per-perturbation performances + if "overall" in test_performance_aggregated: + if "spearman" in test_performance_aggregated["overall"]: + pf = test_performance_aggregated["overall"]["spearman"]["mean"] + logger.info( + f"Mean Spearman's correlation overall: {pf}", + ) + if "auc" in test_performance_aggregated["overall"]: + pf = test_performance_aggregated["overall"]["auc"]["mean"] + logger.info(f"Mean AUC overall: {pf}") + if "per_perturbation" in test_performance_aggregated: + if "spearman" in test_performance_aggregated["per_perturbation"]: + pf = test_performance_aggregated["per_perturbation"]["spearman"]["mean"] + logger.info( + f"Mean Spearman's correlation per perturbation: {pf}", + ) + if "auc" in test_performance_aggregated["per_perturbation"]: + pf = test_performance_aggregated["per_perturbation"]["auc"]["mean"] + logger.info(f"Mean AUC per perturbation: {pf}") + + return test_performance, test_performance_aggregated + + def compute_performances( + self, + test_true_labels: dict[Any, pd.DataFrame], + test_predicted_labels: dict[Any, pd.DataFrame], + performance_per_perturbation: bool | list[bool] | None = None, + metric: RegressionMetricType | list[RegressionMetricType] | None = None, + ) -> dict[str, dict[str, dict[Any, dict[str, float]]]]: + """Compute the performance metrics. + + Parameters + ---------- + test_true_labels : dict[Any, pd.DataFrame] + Dictionary of true labels in the test set(s). + test_predicted_labels : dict[Any, pd.DataFrame] + Dictionary of predicted labels in the test set(s). + performance_per_perturbation : bool | list[bool] | None + Whether to evaluate performance per perturbation (True) or over all + perturbations (False). By default, both per perturbation and overall + performances are calculated. + metric : RegressionMetricType | list[RegressionMetricType] | None + The metric(s) to use for evaluation. Possible values are: "spearman", + "pearson", "r2", "mse" and "mae". By default, all possible values are + calculated. + + Returns + ------- + dict[str, dict[str, dict[Any, dict[str, float]]]] + The first key can be either "per_perturbation" or "overall". The second key + is the metric name. The third key is the split id. + The fourth key is the perturbation name if performance_per_perturbation is True + and "overall" otherwise. The value is the performance metric. + """ + # Define arguments + if performance_per_perturbation is None: + performance_per_perturbation = [True, False] + if metric is None: + metric = ["spearman", "pearson", "r2", "mse", "mae"] + + # Check arguments + if isinstance(metric, str): + metric = [metric] + if isinstance(performance_per_perturbation, bool): + performance_per_perturbation = [performance_per_perturbation] + + # Initiate the dictionary to store the performance + test_performance: dict[str, dict[str, dict[Any, dict[str, float]]]] = {} + + # Compute the different performance metrics + for per_perturbation in performance_per_perturbation: + performance_type = "per_perturbation" if per_perturbation else "overall" + test_performance[performance_type] = {} + for metric_name in metric: + test_performance[performance_type][metric_name] = {} + for split, df_predictions in test_predicted_labels.items(): + # Extract and compare y_true and y_pred + if per_perturbation: + performance_per_split = {} + for label_name in test_true_labels[split].columns: + y_predicted_series = df_predictions[label_name] + y_true_series = test_true_labels[split][label_name] + performance_per_split[label_name] = performance_metric_wrapper( + y_true_series, + y_predicted_series, + metric=metric_name, + ) + else: + # Keep perturbations available in the test set + y_true_df = test_true_labels[split] + y_pred_df = df_predictions + common_columns = y_true_df.columns.intersection(y_pred_df.columns) + y_true_stacked = y_true_df[common_columns].stack() + y_pred_stacked = y_pred_df[common_columns].stack() + performance_per_split = { + "overall": performance_metric_wrapper( + pd.Series(y_true_stacked), + pd.Series(y_pred_stacked), + metric=metric_name, + ) + } + test_performance[performance_type][metric_name][split] = performance_per_split + + return test_performance + + @staticmethod + def aggregate_performances( + test_performance: dict[str, dict[str, dict[Any, dict[str, float]]]], + format_numbers: bool = True, + ) -> dict[str, dict[str, dict[str, float | str]]]: + """Aggregate performances over splits and labels. + + Parameters + ---------- + test_performance : dict[str, dict[str, dict[Any, dict[str, float]]]] + Dictionary of performances per split, per metric and per perturbation + (if applicable). + format_numbers : bool + Whether to format the numbers to 4 decimal places, by default True. + + Returns + ------- + dict[str, dict[str, dict[str, float | str]]] + Dictionary of aggregated performances. + - First key is the performance type ("per_perturbation" or "overall"). + - Second key is the metric name. + - Third key is either "mean" or "std" for the overall performance, and "mean_{perturbation}" or + "std_{perturbation}" for each perturbation if performance_per_perturbation is True. + - The value is the performance metric. + """ + + def format_value(value: float) -> float | str: + return f"{value:.4f}" if format_numbers else value + + aggregated: dict[str, dict[str, dict[str, float | str]]] = {} + for perf_type, metrics in test_performance.items(): + aggregated[perf_type] = {} + for metric, perturbations in metrics.items(): + df_perf = pd.DataFrame(perturbations) + aggregated[perf_type][metric] = { + "mean": format_value(df_perf.mean().mean()), + "std": format_value(df_perf.mean(axis=0).std()), # std over splits + } + if perf_type == "per_perturbation": + df = pd.DataFrame(perturbations).T + aggregated[perf_type][metric].update( + {f"mean_{pert}": format_value(mean) for pert, mean in df.mean().items()} + ) + aggregated[perf_type][metric].update( + {f"std_{pert}": format_value(std) for pert, std in df.std().items()} + ) + return aggregated + + def save_attribute(self, attribute_name: str, output_path: Path) -> None: + """Pickle one of the attributes of the current instance of the class. + + Parameters + ---------- + attribute_name : str + Name of the attribute to save. + output_path : Path + Path of the output file. + """ + logger.info("Saving the results...") + + # Create the parent directory if it does not exist + if not os.path.exists(output_path.parent): + os.makedirs(output_path.parent) + + # Pickle the current instance of the selected attribute + save_pickle(self.__dict__[attribute_name], output_path) + logger.info(f"{attribute_name} saved at {output_path}") + + def save_trainer(self, output_path: Path) -> None: + """Pickle the current instance of the class. + + Parameters + ---------- + output_path : Path + Path of the output file. + """ + logger.info("Saving the trainer...") + + # Create the parent directory if it does not exist + if not os.path.exists(output_path.parent): + os.makedirs(output_path.parent) + + # Pickle the current instance + save_pickle(self, output_path) + logger.info(f"Trainer saved at {output_path}") + + def empty_data(self) -> None: + """Empty the data to save memory.""" + # The rnaseq columns are kept to be able to use predict + self.data.df_rnaseq = pd.DataFrame(columns=self.data.df_rnaseq.columns) + for attr in [ + "df_fingerprints", + "df_labels", + "df_labels_stacked", + "df_perturbation_metadata", + "df_sample_metadata", + "df_sample_metadata_stacked", + ]: + if hasattr(self.data, attr): + delattr(self.data, attr) + + def convert_to_no_ensembling(self, metric: RegressionMetricType | list[RegressionMetricType] | None = None) -> None: + """Update the trainer to use trained models without ensembling. + + This method can only be used after the trainer has been run. It computes + predictions in the test sets using the trained models without ensembling and + updates the prediction performances. + + Parameters + ---------- + metric: RegressionMetricType | list[RegressionMetricType] | None + metric(s) to use for evaluation. + """ + # Check the ensembling status of the model + if self.config_model.ensembling is False: + logger.error("The model is already using no ensembling.") + + # Update the model to use no ensembling + self.config_model.ensembling = False + for trained_model in self.trained_model.values(): + trained_model.ensembling = False + + # Load the data if needed + if not hasattr(self.data, "df_labels"): + self._data = self.load_data() + # Update predictions and performances + self.test_predicted_labels = self.predict_test(data=self.data, split_pair_ids=self.split_pair_ids) + self.test_performance, self.test_performance_aggregated = self.evaluate( + test_true_labels=self.get_test_true_labels(data=self.data, split_pair_ids=self.split_pair_ids), + test_predicted_labels=self.test_predicted_labels, + metric=metric, + ) + + def predict( + self, + X: pd.DataFrame, + X_metadata: pd.DataFrame | None = None, + X_fgpt: pd.DataFrame | None = None, + impute_missing_genes_strategy: str = "zeros", + refit_preprocessor: bool = True, + ensemble_over_splits: bool = True, + ) -> pd.DataFrame | dict[Any, pd.DataFrame]: + """Predict labels in new data. + + (i) Compute the predictions for all samples and all splits. + (ii) Compute the average over all the models' predictions to return a single prediction per sample. + + Parameters + ---------- + X : pd.DataFrame + The new data to predict on. + X_metadata : pd.DataFrame | None + Metadata for the samples in X, by default None. + X_fgpt : pd.DataFrame | None + The fingerprints of the new data, by default None. + impute_missing_genes_strategy : str + The strategy to impute the genes which are given in X but were missing at the training in a given model, + possible options: 'zeros', by default 'zeros'. + refit_preprocessor : bool + Whether to refit the preprocessor on the input data, by default True. This is recommended if the input data + is from a different study than the training data. Re-fitting the preprocessor can be seen as a simple data + alignment procedure. + ensemble_over_splits : bool + Whether to ensemble over the splits, by default True. + + Returns + ------- + pd.DataFrame | dict[Any, pd.DataFrame] + The predicted labels. + If ensemble_over_splits is True, the average of the predictions over all + splits is returned. Otherwise, the predictions for each split are returned, + as a dictionary with the split id as key. + + Raises + ------ + ValueError + If the strategy to impute missing genes is not supported. + """ + # Compute the predictions for all samples and all splits + predictions = {} + + # Create dummy X_metadata if needed + if X_metadata is None: + X_metadata = pd.DataFrame(index=X.index) + + # Ensure that X and X_metadata ids are in the same order + X_columns = X.columns + X = X.loc[X_metadata.index, X_columns.isin(self.data.df_rnaseq.columns)] + X_metadata = X_metadata.loc[X.index] + + # Impute missing genes if needed + rnaseq_columns = set(self.data.df_rnaseq.columns) + missing_genes = rnaseq_columns - set(X_columns) + if len(missing_genes) > 0: + # Check that there are common genes between the input and the model + if len(rnaseq_columns.intersection(X_columns)) == 0: + raise ValueError("No common genes between the input and the model.") + + # Report the number of missing genes + logger.info( + f"Imputing {len(missing_genes)} of the {len(rnaseq_columns)} required genes that are not available in " + "the input data." + ) + + missing_genes_list = list(missing_genes) + if impute_missing_genes_strategy == "zeros": + X = pd.concat([X, pd.DataFrame(0, index=X.index, columns=missing_genes_list)], axis=1) + else: + raise ValueError( + f"Strategy {impute_missing_genes_strategy} is not supported for imputing missing genes." + ) + + # iterate through every model + for split in self.split_pair_ids: + if refit_preprocessor: + # Refit the preprocessor that was used during training + preprocessor = copy.deepcopy(self.trained_model[split].trained_preprocessor) + # Remove _rnaseq suffix for preprocessing + X_no_suffix = X.copy() + X_no_suffix.columns = X_no_suffix.columns.str.replace("_rnaseq", "", regex=False) + if preprocessor is not None: + preprocessor.fit(X_no_suffix) + X_preprocessed = preprocessor.transform(X_no_suffix) + else: + X_preprocessed = X_no_suffix + # Add _rnaseq suffix back + X_preprocessed = X_preprocessed.copy() + X_preprocessed.columns = X_preprocessed.columns + "_rnaseq" + else: + X_preprocessed = X + + # Fingerprints are always loaded in the trainer but only used by some models + predictions[split] = self.trained_model[split].predict( + X=X_preprocessed, + X_fgpt=X_fgpt, + X_metadata=X_metadata, + preprocessor_transform=not refit_preprocessor, + ) + + if ensemble_over_splits: + # Average all the values from the keys in the dictionary + summed_predictions = sum(predictions.values()) + predictions_df = summed_predictions / len(predictions) + return predictions_df + + # Return the predictions for each split + return predictions diff --git a/src/leap/utils/__init__.py b/src/leap/utils/__init__.py new file mode 100644 index 0000000..5edccde --- /dev/null +++ b/src/leap/utils/__init__.py @@ -0,0 +1,9 @@ +"""Init file for the utils module.""" + +from .config_utils import instantiate +from .device import get_device +from .io import load_pickle, save_pickle +from .seed import seed_everything + + +__all__ = ["get_device", "instantiate", "load_pickle", "save_pickle", "seed_everything"] diff --git a/src/leap/utils/config_utils.py b/src/leap/utils/config_utils.py new file mode 100644 index 0000000..2914179 --- /dev/null +++ b/src/leap/utils/config_utils.py @@ -0,0 +1,61 @@ +"""Utils for handling configurations.""" + +import functools +import importlib +from collections import abc +from copy import deepcopy +from typing import Any + +from ml_collections import config_dict + + +def _get_and_pop(config: config_dict.ConfigDict, key: str) -> Any: + val = None + if key in config: + val = config[key] + del config[key] + return val + + +def get_config_dict_copy(config: config_dict.ConfigDict) -> config_dict.ConfigDict: + """Return an editable copy of an ml_collections configuration dictionary.""" + return config_dict.ConfigDict(deepcopy(config)).unlock() + + +def load_module(module_path: str | Any) -> abc.Callable: + """Load a module from its string representation.""" + if isinstance(module_path, str): + module_name, class_name = module_path.rsplit(".", 1) + return getattr(importlib.import_module(module_name), class_name) + return module_path + + +def instantiate(config: config_dict.ConfigDict, force_partial: bool = False) -> Any: + """Process config entries that contain _target_ and _partial_ options.""" + if not isinstance(config, config_dict.ConfigDict): + return config + config = get_config_dict_copy(config) + + if config.get("_skip_instantiate_"): + return config + + target = _get_and_pop(config, "_target_") + target = load_module(target) + partial = _get_and_pop(config, "_partial_") or force_partial + + for key in config: + val = instantiate(config[key]) + del config[key] + config[key] = val + + if target is not None: + if not partial: + try: + return target(**config) + except TypeError: + return target.remote(**config) # type: ignore + except Exception as e: + raise ValueError(f"Failed to instantiate {target} with config {config}") from e + else: + return functools.partial(target, **config) + return config diff --git a/src/leap/utils/device.py b/src/leap/utils/device.py new file mode 100644 index 0000000..a095fd8 --- /dev/null +++ b/src/leap/utils/device.py @@ -0,0 +1,72 @@ +"""Utility functions for PyTorch device detection and management.""" + +import torch + + +def get_device(device: str | None = None) -> str: # noqa: PLR0911 + """Get the appropriate PyTorch device for computation. + + This function provides centralized device detection with support for: + - CUDA (NVIDIA GPUs) + - MPS (Apple Silicon M1/M2/M3) + - CPU (fallback) + + Parameters + ---------- + device : str | None, optional + Requested device as a string ("cuda", "mps", or "cpu"). + If None, automatically detects the best available device. + If specified but not available, falls back to the best available device. + + Returns + ------- + str + Device string that can be used with PyTorch operations. + One of: "cuda", "mps", or "cpu". + + Examples + -------- + >>> device = get_device() # Auto-detect best available + >>> model.to(device) + >>> + >>> device = get_device("cuda") # Request specific device + >>> tensor = torch.tensor([1, 2, 3]).to(device) + + Notes + ----- + - Priority order: CUDA > MPS > CPU + - Returns a string (not torch.device) for simplicity and consistency + - PyTorch accepts both strings and torch.device objects in .to() methods + """ + # If no device specified, auto-detect + if device is None: + if torch.cuda.is_available(): + return "cuda" + elif torch.backends.mps.is_available(): + return "mps" + else: + return "cpu" + + # Validate requested device + device_lower = device.lower() + + if device_lower == "cuda": + if torch.cuda.is_available(): + return "cuda" + else: + # Fall back to MPS or CPU + return get_device(None) + + elif device_lower == "mps": + if torch.backends.mps.is_available(): + return "mps" + else: + # Fall back to CPU + return "cpu" + + elif device_lower == "cpu": + return "cpu" + + else: + # Invalid device specified, auto-detect + return get_device(None) diff --git a/src/leap/utils/io.py b/src/leap/utils/io.py new file mode 100644 index 0000000..eb6ed17 --- /dev/null +++ b/src/leap/utils/io.py @@ -0,0 +1,36 @@ +"""I/O utilities.""" + +import pickle +from pathlib import Path +from typing import Any + + +def load_pickle(path: Path) -> Any: + """Load a pickle file. + + Parameters + ---------- + path : Path + Path to the pickle file. + + Returns + ------- + Any + The loaded object. + """ + with open(path, "rb") as f: + return pickle.load(f) + + +def save_pickle(object: Any, path: Path) -> None: + """Save an object to a pickle file. + + Parameters + ---------- + object : Any + Object to save. + path : Path + Path to the pickle file. + """ + with open(path, "wb") as f: + pickle.dump(object, f, protocol=pickle.HIGHEST_PROTOCOL) diff --git a/src/leap/utils/seed.py b/src/leap/utils/seed.py new file mode 100644 index 0000000..b883832 --- /dev/null +++ b/src/leap/utils/seed.py @@ -0,0 +1,20 @@ +"""Source code to fix every seed.""" + +import random + +import numpy as np +import torch + + +def seed_everything(seed: int) -> None: + """Set the seed for generating random numbers in PyTorch, numpy and Python. + + Parameters + ---------- + seed : int + The desired seed. + """ + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) diff --git a/src/tests/configs/__init__.py b/src/tests/configs/__init__.py new file mode 100644 index 0000000..66c6821 --- /dev/null +++ b/src/tests/configs/__init__.py @@ -0,0 +1 @@ +"""Tests for configs module.""" diff --git a/src/tests/configs/test_config_perturbation_model.py b/src/tests/configs/test_config_perturbation_model.py new file mode 100644 index 0000000..cb2b540 --- /dev/null +++ b/src/tests/configs/test_config_perturbation_model.py @@ -0,0 +1,53 @@ +"""Test config_perturbation_model.py for consistency.""" + +from configs import config_perturbation_model + + +class TestConfigPerturbationModel: + """Test that all config dictionaries are consistent.""" + + def test_all_dicts_have_same_keys(self) -> None: + """Test that all config dictionaries have the same model IDs as keys.""" + dict_names = [ + name + for name in dir(config_perturbation_model) + if name.isupper() and isinstance(getattr(config_perturbation_model, name), dict) + ] + + if len(dict_names) < 2: + return + + # All dicts should have the same keys + reference_keys = set(getattr(config_perturbation_model, dict_names[0]).keys()) + + for dict_name in dict_names[1:]: + current_dict = getattr(config_perturbation_model, dict_name) + assert set(current_dict.keys()) == reference_keys, ( + f"{dict_name} has keys {set(current_dict.keys())} but expected {reference_keys}" + ) + + def test_within_dict_type_consistency(self) -> None: + """Test that within each dictionary, all values have consistent types.""" + dict_names = [ + name + for name in dir(config_perturbation_model) + if name.isupper() and isinstance(getattr(config_perturbation_model, name), dict) + ] + + for dict_name in dict_names: + current_dict = getattr(config_perturbation_model, dict_name) + if not current_dict: + continue + + # Get types of all values + value_types = {} + for key, value in current_dict.items(): + # Normalize None and dict as compatible + if value is None or isinstance(value, dict): + value_types[key] = "dict_or_none" + else: + value_types[key] = type(value).__name__ + + # Check all values in this dict have the same type + unique_types = set(value_types.values()) + assert len(unique_types) == 1, f"{dict_name} has inconsistent value types: {value_types}" diff --git a/src/tests/configs/test_config_regression_model.py b/src/tests/configs/test_config_regression_model.py new file mode 100644 index 0000000..4f288fb --- /dev/null +++ b/src/tests/configs/test_config_regression_model.py @@ -0,0 +1,47 @@ +"""Test config_regression_model.py for valid ConfigDict structures.""" + +from ml_collections import config_dict + +from configs import config_regression_model + + +class TestConfigRegressionModel: + """Test regression model configurations.""" + + def test_all_main_dicts_have_same_keys(self) -> None: + """Test that REGRESSION_MODEL and HPT_TUNING_PARAM_GRID have same model keys.""" + regression_keys = set(config_regression_model.REGRESSION_MODEL.keys()) + hpt_keys = set(config_regression_model.HPT_TUNING_PARAM_GRID.keys()) + + assert regression_keys == hpt_keys, ( + f"Model keys should match across all dicts. " + f"REGRESSION_MODEL: {regression_keys}, " + f"HPT_TUNING_PARAM_GRID: {hpt_keys}, " + ) + + def test_all_regression_configs_have_target(self) -> None: + """Test that all regression model configs have _target_ key.""" + for key, value in config_regression_model.REGRESSION_MODEL.items(): + assert isinstance(value, config_dict.ConfigDict), f"{key} should be a ConfigDict" + assert "_target_" in value, f"{key} should have '_target_' key" + assert callable(value["_target_"]), f"{key}['_target_'] should be a class" + + def test_mlp_variants_have_same_structure(self) -> None: + """Test that MLP model variants have the same parameter structure.""" + mlp_models = [k for k in config_regression_model.REGRESSION_MODEL.keys() if "mlp" in k or "dnn" in k] + + if len(mlp_models) < 2: + return + + # Get parameter sets for each MLP model + param_sets = {} + for model in mlp_models: + param_sets[model] = set(config_regression_model.REGRESSION_MODEL[model].keys()) - {"_target_"} + + # Check they all have the same parameters + reference_params = param_sets[mlp_models[0]] + for model in mlp_models[1:]: + assert param_sets[model] == reference_params, ( + f"{model} and {mlp_models[0]} should have same parameters. " + f"Difference: {param_sets[model].symmetric_difference(reference_params)}" + ) diff --git a/src/tests/configs/test_config_rpz_model.py b/src/tests/configs/test_config_rpz_model.py new file mode 100644 index 0000000..aa6b4d1 --- /dev/null +++ b/src/tests/configs/test_config_rpz_model.py @@ -0,0 +1,28 @@ +"""Test config_rpz_model.py for valid ConfigDict structures.""" + +from ml_collections import config_dict + +from configs import config_rpz_model + + +class TestConfigRpzModel: + """Test RPZ_MODEL configuration.""" + + def test_all_configs_have_target(self) -> None: + """Test that all configs have _target_ key.""" + for key, value in config_rpz_model.RPZ_MODEL.items(): + assert isinstance(value, config_dict.ConfigDict), f"{key} should be a ConfigDict" + assert "_target_" in value, f"{key} should have '_target_' key" + assert callable(value["_target_"]), f"{key}['_target_'] should be a class or function" + + def test_mae_and_ae_have_same_parameters(self) -> None: + """Test that MAE and AE configs have the same parameter structure.""" + if "mae" not in config_rpz_model.RPZ_MODEL or "ae" not in config_rpz_model.RPZ_MODEL: + return + + mae_keys = set(config_rpz_model.RPZ_MODEL["mae"].keys()) - {"_target_"} + ae_keys = set(config_rpz_model.RPZ_MODEL["ae"].keys()) - {"_target_"} + + assert mae_keys == ae_keys, ( + f"MAE and AE should have same parameters. MAE only: {mae_keys - ae_keys}, AE only: {ae_keys - mae_keys}" + ) diff --git a/src/tests/configs/test_config_trainer.py b/src/tests/configs/test_config_trainer.py new file mode 100644 index 0000000..e67b0e1 --- /dev/null +++ b/src/tests/configs/test_config_trainer.py @@ -0,0 +1,71 @@ +"""Test config_trainer.py for consistency.""" + +from configs import config_trainer + + +class TestConfigTrainer: + """Test that all config dictionaries are consistent.""" + + def test_all_dicts_have_same_keys(self) -> None: + """Test that all config dictionaries have the same task IDs as keys.""" + dict_names = [ + name for name in dir(config_trainer) if name.isupper() and isinstance(getattr(config_trainer, name), dict) + ] + + if len(dict_names) < 2: + return + + # All dicts should have the same keys + reference_keys = set(getattr(config_trainer, dict_names[0]).keys()) + + for dict_name in dict_names[1:]: + current_dict = getattr(config_trainer, dict_name) + assert set(current_dict.keys()) == reference_keys, ( + f"{dict_name} has keys {set(current_dict.keys())} but expected {reference_keys}" + ) + + def test_within_dict_type_consistency(self) -> None: + """Test that within each dictionary, all values have consistent types (allowing for str/list[str] mix).""" + dict_names = [ + name for name in dir(config_trainer) if name.isupper() and isinstance(getattr(config_trainer, name), dict) + ] + + for dict_name in dict_names: + current_dict = getattr(config_trainer, dict_name) + if not current_dict: + continue + + # Get types of all values + value_types = {} + for key, value in current_dict.items(): + if value is None: + value_types[key] = "None" + elif isinstance(value, list): + value_types[key] = "list" + elif isinstance(value, str): + value_types[key] = "str" + elif isinstance(value, (int, bool)): + value_types[key] = type(value).__name__ + else: + value_types[key] = type(value).__name__ + + # Check all values in this dict have compatible types + unique_types = set(value_types.values()) + + # These combinations are allowed: + # - All same type + # - str + list (common: single study vs multiple studies) + # - str + None or list + None + allowed_combos = [ + {"str"}, + {"list"}, + {"str", "list"}, + {"str", "None"}, + {"list", "None"}, + {"str", "list", "None"}, + {"int"}, + {"bool"}, + ] + + is_valid = len(unique_types) == 1 or unique_types in allowed_combos + assert is_valid, f"{dict_name} has inconsistent value types: {value_types}" diff --git a/src/tests/configs/test_get_config_functions.py b/src/tests/configs/test_get_config_functions.py new file mode 100644 index 0000000..cb240e8 --- /dev/null +++ b/src/tests/configs/test_get_config_functions.py @@ -0,0 +1,140 @@ +"""Test get_config_*.py functions return valid configurations.""" + +import inspect + +import pytest + +from configs.get_config import get_config +from configs.get_config_data import get_config_data +from configs.get_config_models import get_config_model +from configs.get_config_split import get_config_split + + +def check_config_params_valid(config, config_name="config"): + """Recursively check that config parameters match the _target_ class signature. + + This catches issues where config has parameters the target class doesn't accept. + """ + if not hasattr(config, "_target_"): + return + + target = config["_target_"] + + # Get signature + try: + if inspect.isclass(target): + sig = inspect.signature(target.__init__) + else: + sig = inspect.signature(target) + except (ValueError, TypeError): + return + + # Get valid parameter names (excluding self) + valid_params = {p for p in sig.parameters.keys() if p != "self"} + + # Check if target accepts **kwargs + has_var_keyword = any(p.kind == inspect.Parameter.VAR_KEYWORD for p in sig.parameters.values()) + + # Get config parameters (excluding _target_ and _partial_) + config_params = {k for k in config.keys() if not k.startswith("_")} + + # If no **kwargs, check for invalid parameters + if not has_var_keyword: + invalid_params = config_params - valid_params + assert not invalid_params, ( + f"{config_name} (target={target.__name__}) has invalid parameters: {invalid_params}. " + f"Valid parameters are: {sorted(valid_params)}" + ) + + +class TestGetConfigData: + """Test get_config_data functions.""" + + def test_basic_config_generation(self) -> None: + """Test that get_config_data generates valid configs.""" + config = get_config_data( + source_domain_studies="DepMap_23Q4", + source_domain_label="gene_dependency", + list_of_perturbations="perturbations_task_1", + filter_available_fingerprints=True, + normalization="tpm", + list_of_genes="most_variant_genes", + ) + + assert "source_domain_data" in config + assert "_target_" in config.source_domain_data + check_config_params_valid(config.source_domain_data, "source_domain_data") + + +class TestGetConfigSplit: + """Test get_config_split functions.""" + + @pytest.mark.parametrize("test_set_type", ["sample", "perturbation", "tissue", "transfer_learning"]) + def test_split_config_generation(self, test_set_type: str) -> None: + """Test that get_config_split generates valid configs.""" + config = get_config_split(test_set_type=test_set_type, training_split_count=10) + + assert "_target_" in config + assert "_partial_" in config + # Partial configs will be called with additional args later, so we can't validate them fully + + +class TestGetConfigModels: + """Test get_config_models functions.""" + + @pytest.mark.parametrize("pred_model_type", ["multi_label", "perturbation_specific", "pan_perturbation"]) + def test_model_config_generation(self, pred_model_type: str) -> None: + """Test that get_config_model generates valid configs.""" + pred_model_name = "knn_regressor" if pred_model_type == "multi_label" else "elastic_net_regressor" + + config = get_config_model( + pred_model_type=pred_model_type, + pred_model_name=pred_model_name, + list_of_genes="most_variant_genes", + normalization="tpm", + rpz_model_name="pca", + use_trained_preprocessor=False, + use_trained_rpz=False, + pretrained_data="depmap", + rpz_random_state=0, + fgps_dim=256, + ensembling=True, + ensembling_save_models_to_disk=False, + use_ray=False, + ray_remote_params=None, + ) + + assert "_target_" in config + check_config_params_valid(config, "model") + + +class TestGetConfig: + """Test the full get_config function.""" + + def test_full_config_generation(self) -> None: + """Test that get_config generates a complete valid config.""" + config = get_config(task_id="1", model_id="mae_ps_enet", rpz_random_state=0) + + # Check main structure + assert "source_domain_data" in config + assert "data_split" in config + assert "model" in config + + # Check all have _target_ + assert "_target_" in config.source_domain_data + assert "_target_" in config.data_split + assert "_target_" in config.model + + # Validate parameter signatures + check_config_params_valid(config.source_domain_data, "source_domain_data") + check_config_params_valid(config.model, "model") + + def test_invalid_task_id(self) -> None: + """Test that invalid task_id raises error.""" + with pytest.raises(ValueError, match="not recognized"): + get_config(task_id="invalid", model_id="mae_ps_enet", rpz_random_state=0) + + def test_invalid_model_id(self) -> None: + """Test that invalid model_id raises error.""" + with pytest.raises(ValueError, match="not recognized"): + get_config(task_id="1", model_id="invalid", rpz_random_state=0) diff --git a/src/tests/conftest.py b/src/tests/conftest.py new file mode 100644 index 0000000..59d541b --- /dev/null +++ b/src/tests/conftest.py @@ -0,0 +1,23 @@ +"""Pytest configuration for LEAP tests. + +This module configures pytest to avoid segmentation faults caused by +multi-threaded operations in PyTorch and numpy libraries. +""" + +import os + + +# Set environment variables to prevent threading issues that cause segfaults +# These must be set before importing numpy/torch +os.environ["OMP_NUM_THREADS"] = "1" +os.environ["MKL_NUM_THREADS"] = "1" +os.environ["OPENBLAS_NUM_THREADS"] = "1" +os.environ["VECLIB_MAXIMUM_THREADS"] = "1" +os.environ["NUMEXPR_NUM_THREADS"] = "1" + +# Import torch after setting environment variables +import torch + + +# Set torch to use single thread +torch.set_num_threads(1) diff --git a/src/tests/leap/__init__.py b/src/tests/leap/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/tests/leap/data/__init__.py b/src/tests/leap/data/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/tests/leap/data/test_data.py b/src/tests/leap/data/test_data.py new file mode 100644 index 0000000..4c80326 --- /dev/null +++ b/src/tests/leap/data/test_data.py @@ -0,0 +1,157 @@ +"""Tests for the data module.""" + +import tempfile +from pathlib import Path + +import numpy as np +import pandas as pd +import pytest + +from leap.data.preprocessor import SCALERS, OmicsPreprocessor + + +class TestPreprocessor: + """Test OmicsPreprocessor class.""" + + @pytest.fixture + def sample_data(self): + """Create sample data for testing.""" + np.random.seed(42) + # Create data with 50 samples and 100 genes + data = pd.DataFrame(np.random.exponential(scale=10, size=(50, 100)), columns=[f"gene_{i}" for i in range(100)]) + return data + + def test_preprocessor_initialization(self): + """Test preprocessor initialization.""" + preprocessor = OmicsPreprocessor(scaling_method="min_max", max_genes=50, log_scaling=True) + + assert preprocessor.scaling_method == "min_max" + assert preprocessor.max_genes == 50 + assert preprocessor.log_scaling is True + + def test_preprocessor_invalid_scaler_raises_error(self): + """Test that invalid scaler raises ValueError.""" + with pytest.raises(ValueError, match="Scaling method must be"): + OmicsPreprocessor(scaling_method="invalid_scaler") + + def test_preprocessor_fit_transform_min_max(self, sample_data): + """Test fit_transform with min_max scaling.""" + preprocessor = OmicsPreprocessor(scaling_method="min_max", max_genes=-1, log_scaling=False) + + transformed = preprocessor.fit_transform(sample_data) + + # Check that data is scaled between 0 and 1 (with tolerance for floating point) + assert transformed.min().min() >= -1e-10 + assert transformed.max().max() <= 1 + 1e-10 + assert transformed.shape == sample_data.shape + + def test_preprocessor_fit_transform_mean_std(self, sample_data): + """Test fit_transform with mean_std scaling.""" + preprocessor = OmicsPreprocessor(scaling_method="mean_std", max_genes=-1, log_scaling=False) + + transformed = preprocessor.fit_transform(sample_data) + + # Check that data is standardized (mean ~ 0, std ~ 1) + assert abs(transformed.mean().mean()) < 0.1 + assert abs(transformed.std().mean() - 1.0) < 0.1 + + def test_preprocessor_log_scaling(self, sample_data): + """Test log scaling.""" + preprocessor = OmicsPreprocessor(scaling_method="identity", max_genes=-1, log_scaling=True) + + transformed = preprocessor.fit_transform(sample_data) + + # Check that log transform was applied + # Note: columns are sorted by preprocessor + expected = np.log1p(sample_data).sort_index(axis=1) + pd.testing.assert_frame_equal(transformed, expected, check_dtype=False) + + def test_preprocessor_gene_selection(self, sample_data): + """Test gene selection based on variance.""" + max_genes = 20 + preprocessor = OmicsPreprocessor(scaling_method="identity", max_genes=max_genes, log_scaling=False) + + transformed = preprocessor.fit_transform(sample_data) + + # Should keep only max_genes + assert transformed.shape[1] == max_genes + assert len(preprocessor.columns_to_keep) == max_genes + + def test_preprocessor_gene_list_source_file(self, sample_data): + """Test gene selection from file.""" + # Preprocessor reads first column and intersects with available genes + selected_genes = ["gene_0", "gene_1", "gene_2", "gene_3"] + + with tempfile.TemporaryDirectory() as tmpdir: + gene_list_file = Path(tmpdir) / "genes.csv" + pd.DataFrame(selected_genes).to_csv(gene_list_file, index=False, header=False) + + preprocessor = OmicsPreprocessor( + scaling_method="identity", max_genes=-1, log_scaling=False, gene_list_source=str(gene_list_file) + ) + + transformed = preprocessor.fit_transform(sample_data) + + assert transformed.shape[1] == len(selected_genes) + assert all(col in selected_genes for col in transformed.columns) + + def test_preprocessor_gene_list_source_list(self, sample_data): + """Test gene selection from list.""" + selected_genes = ["gene_0", "gene_5", "gene_10"] + + preprocessor = OmicsPreprocessor( + scaling_method="identity", max_genes=-1, log_scaling=False, gene_list_source=selected_genes + ) + + transformed = preprocessor.fit_transform(sample_data) + + assert transformed.shape[1] == len(selected_genes) + + def test_preprocessor_fit_then_transform(self, sample_data): + """Test separate fit and transform calls.""" + preprocessor = OmicsPreprocessor(scaling_method="min_max", max_genes=-1, log_scaling=True) + + # Split data + train_data = sample_data.iloc[:40] + test_data = sample_data.iloc[40:] + + # Fit on train + preprocessor.fit(train_data) + + # Transform both + train_transformed = preprocessor.transform(train_data) + test_transformed = preprocessor.transform(test_data) + + assert train_transformed.shape == train_data.shape + assert test_transformed.shape == test_data.shape + + def test_preprocessor_columns_consistency(self, sample_data): + """Test that columns are consistent between fit and transform.""" + preprocessor = OmicsPreprocessor(scaling_method="identity", max_genes=50, log_scaling=False) + + preprocessor.fit(sample_data) + columns_after_fit = preprocessor.columns_to_keep.copy() + + transformed = preprocessor.transform(sample_data) + + assert preprocessor.columns_to_keep == columns_after_fit + assert list(transformed.columns) == sorted(columns_after_fit) + + def test_preprocessor_rank_genes(self, sample_data): + """Test gene ranking by variance.""" + preprocessor = OmicsPreprocessor(scaling_method="identity", max_genes=-1, log_scaling=False) + + gene_ranks = preprocessor.rank_genes(sample_data) + + # Check that all genes are ranked + assert len(gene_ranks) == sample_data.shape[1] + # Check that ranks are unique + assert len(set(gene_ranks)) == len(gene_ranks) + + def test_all_scalers_work(self, sample_data): + """Test that all scalers in SCALERS work.""" + for scaler_name in SCALERS: + preprocessor = OmicsPreprocessor(scaling_method=scaler_name, max_genes=-1, log_scaling=False) + + transformed = preprocessor.fit_transform(sample_data) + assert transformed.shape == sample_data.shape diff --git a/src/tests/leap/metrics/__init__.py b/src/tests/leap/metrics/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/tests/leap/metrics/test_metrics.py b/src/tests/leap/metrics/test_metrics.py new file mode 100644 index 0000000..3a6366f --- /dev/null +++ b/src/tests/leap/metrics/test_metrics.py @@ -0,0 +1,183 @@ +"""Tests for the metrics module.""" + +import numpy as np +import pandas as pd +import pytest +from scipy.stats import pearsonr, spearmanr + +from leap.metrics.regression_metrics import ( + REGRESSION_METRICS, + performance_metric, + performance_metric_wrapper, +) + + +class TestPerformanceMetric: + """Test performance_metric function.""" + + def test_spearman_correlation(self): + """Test Spearman correlation metric.""" + y_true = np.array([1, 2, 3, 4, 5]) + y_pred = np.array([1.1, 2.2, 2.9, 4.1, 5.2]) + + result = performance_metric(y_true, y_pred, metric="spearman") + expected, _ = spearmanr(y_true, y_pred) + + assert abs(result - expected) < 1e-6 + + def test_pearson_correlation(self): + """Test Pearson correlation metric.""" + y_true = np.array([1, 2, 3, 4, 5]) + y_pred = np.array([1.1, 2.2, 2.9, 4.1, 5.2]) + + result = performance_metric(y_true, y_pred, metric="pearson") + expected, _ = pearsonr(y_true, y_pred) + + assert abs(result - expected) < 1e-6 + + def test_r2_score(self): + """Test Rยฒ score metric.""" + y_true = np.array([1, 2, 3, 4, 5]) + y_pred = np.array([1.1, 2.2, 2.9, 4.1, 5.2]) + + result = performance_metric(y_true, y_pred, metric="r2") + + # Rยฒ should be high for good predictions + assert result > 0.9 + + def test_mse(self): + """Test Mean Squared Error metric.""" + y_true = np.array([1, 2, 3, 4, 5]) + y_pred = np.array([1, 2, 3, 4, 5]) + + result = performance_metric(y_true, y_pred, metric="mse") + + # MSE should be 0 for perfect predictions + assert result == 0.0 + + def test_mae(self): + """Test Mean Absolute Error metric.""" + y_true = np.array([1, 2, 3, 4, 5]) + y_pred = np.array([1, 2, 3, 4, 5]) + + result = performance_metric(y_true, y_pred, metric="mae") + + # MAE should be 0 for perfect predictions + assert result == 0.0 + + def test_constant_predictions_spearman(self): + """Test that constant predictions return 0 for Spearman.""" + y_true = np.array([1, 2, 3, 4, 5]) + y_pred = np.array([2, 2, 2, 2, 2]) # Constant predictions + + result = performance_metric(y_true, y_pred, metric="spearman") + + # Spearman correlation for constant predictions should be 0 + assert result == 0.0 + + def test_constant_predictions_pearson(self): + """Test that constant predictions return 0 for Pearson.""" + y_true = np.array([1, 2, 3, 4, 5]) + y_pred = np.array([2, 2, 2, 2, 2]) # Constant predictions + + result = performance_metric(y_true, y_pred, metric="pearson") + + # Pearson correlation for constant predictions should be 0 + assert result == 0.0 + + def test_invalid_metric_raises_error(self): + """Test that invalid metric raises ValueError.""" + y_true = np.array([1, 2, 3, 4, 5]) + y_pred = np.array([1.1, 2.2, 2.9, 4.1, 5.2]) + + with pytest.raises(ValueError, match="Unsupported metric"): + performance_metric(y_true, y_pred, metric="invalid_metric") + + def test_all_metrics_are_valid(self): + """Test that all metrics in REGRESSION_METRICS work.""" + y_true = np.array([1, 2, 3, 4, 5]) + y_pred = np.array([1.1, 2.2, 2.9, 4.1, 5.2]) + + for metric in REGRESSION_METRICS: + result = performance_metric(y_true, y_pred, metric=metric) + assert isinstance(result, (float, np.floating)) + + +class TestPerformanceMetricWrapper: + """Test performance_metric_wrapper function.""" + + def test_wrapper_with_series(self): + """Test wrapper with pandas Series.""" + y_true = pd.Series([1, 2, 3, 4, 5], index=["a", "b", "c", "d", "e"]) + y_pred = pd.Series([1.1, 2.2, 2.9, 4.1, 5.2], index=["a", "b", "c", "d", "e"]) + + result = performance_metric_wrapper(y_true, y_pred, metric="spearman") + + assert isinstance(result, (float, np.floating)) + assert result > 0.9 + + def test_wrapper_handles_missing_values(self): + """Test that wrapper correctly handles missing values.""" + y_true = pd.Series([1, 2, np.nan, 4, 5], index=["a", "b", "c", "d", "e"]) + y_pred = pd.Series([1.1, 2.2, 2.9, 4.1, 5.2], index=["a", "b", "c", "d", "e"]) + + result = performance_metric_wrapper(y_true, y_pred, metric="spearman") + + # Should compute metric only on non-missing values + assert isinstance(result, (float, np.floating)) + + def test_wrapper_with_multiindex(self): + """Test wrapper with MultiIndex (sample, perturbation).""" + index = pd.MultiIndex.from_tuples( + [("sample1", "geneA"), ("sample1", "geneB"), ("sample2", "geneA"), ("sample2", "geneB")], + names=["sample", "perturbation"], + ) + y_true = pd.Series([1, 2, 3, 4], index=index) + y_pred = pd.Series([1.1, 2.1, 3.1, 4.1], index=index) + + # Test overall metric + result = performance_metric_wrapper(y_true, y_pred, metric="spearman", per_perturbation=False) + assert isinstance(result, (float, np.floating)) + + def test_wrapper_per_perturbation(self): + """Test per-perturbation metric calculation.""" + index = pd.MultiIndex.from_tuples( + [ + ("sample1", "geneA"), + ("sample2", "geneA"), + ("sample3", "geneA"), + ("sample1", "geneB"), + ("sample2", "geneB"), + ("sample3", "geneB"), + ], + names=["sample", "perturbation"], + ) + y_true = pd.Series([1, 2, 3, 4, 5, 6], index=index) + y_pred = pd.Series([1.1, 2.1, 3.1, 4.1, 5.1, 6.1], index=index) + + result = performance_metric_wrapper(y_true, y_pred, metric="spearman", per_perturbation=True) + + # Should return average across perturbations + assert isinstance(result, (float, np.floating)) + + def test_wrapper_different_metrics(self): + """Test wrapper with different metrics.""" + y_true = pd.Series([1, 2, 3, 4, 5]) + y_pred = pd.Series([1.1, 2.2, 2.9, 4.1, 5.2]) + + for metric in REGRESSION_METRICS: + result = performance_metric_wrapper(y_true, y_pred, metric=metric) + assert isinstance(result, (float, np.floating)) + + def test_wrapper_perfect_prediction(self): + """Test wrapper with perfect predictions.""" + y_true = pd.Series([1, 2, 3, 4, 5]) + y_pred = pd.Series([1, 2, 3, 4, 5]) + + spearman_result = performance_metric_wrapper(y_true, y_pred, metric="spearman") + pearson_result = performance_metric_wrapper(y_true, y_pred, metric="pearson") + mse_result = performance_metric_wrapper(y_true, y_pred, metric="mse") + + assert abs(spearman_result - 1.0) < 1e-10 + assert abs(pearson_result - 1.0) < 1e-10 + assert abs(mse_result - 0.0) < 1e-10 diff --git a/src/tests/leap/pipelines/__init__.py b/src/tests/leap/pipelines/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/tests/leap/pipelines/test_pipelines.py b/src/tests/leap/pipelines/test_pipelines.py new file mode 100644 index 0000000..c7a239a --- /dev/null +++ b/src/tests/leap/pipelines/test_pipelines.py @@ -0,0 +1,435 @@ +"""Tests for the pipelines module.""" + +import tempfile +from pathlib import Path + +import numpy as np +import pandas as pd +import pytest +from sklearn.linear_model import Ridge + +from leap.data.preprocessor import OmicsPreprocessor +from leap.pipelines.perturbation_pipeline import ( + FOLD_PREFIX, + FULL_TRAINING_KEY, + PerturbationPipeline, + define_model_params, +) +from leap.regression_models import KnnRegressor +from leap.representation_models import PCA + + +class TestPerturbationPipeline: + """Test PerturbationPipeline class.""" + + @pytest.fixture + def sample_data(self): + """Create sample data for testing.""" + np.random.seed(42) + n_samples = 50 + n_features = 20 + n_perturbations = 5 + + X = pd.DataFrame( + np.random.exponential(scale=10, size=(n_samples, n_features)), + columns=[f"feature_{i}" for i in range(n_features)], + index=[f"sample_{i}" for i in range(n_samples)], + ) + + y = pd.DataFrame( + np.random.randn(n_samples, n_perturbations), + columns=[f"pert_{i}" for i in range(n_perturbations)], + index=[f"sample_{i}" for i in range(n_samples)], + ) + + # Add some NaN values + y.iloc[0:5, 0] = np.nan + y.iloc[10:15, 1] = np.nan + + X_metadata = pd.DataFrame({"tissue": np.random.choice(["Lung", "Breast"], n_samples)}, index=X.index) + + return X, y, X_metadata + + def test_pipeline_initialization_minimal(self): + """Test minimal pipeline initialization.""" + pipeline = PerturbationPipeline( + preprocessor_model_rnaseq=None, + rpz_model_rnaseq=None, + regression_model_base_instance=KnnRegressor(n_sample_neighbors=5, weights="uniform"), + hpt_tuning_cv_split=None, + hpt_tuning_param_grid=None, + hpt_tuning_score=None, + fgpt_rpz_model=None, + one_model_per_perturbation=True, + ensembling=False, + ) + + assert pipeline.one_model_per_perturbation is True + assert pipeline.ensembling is False + assert pipeline.trained_preprocessor is None + assert pipeline.trained_rpz_model is None + + def test_pipeline_initialization_with_preprocessor(self): + """Test pipeline initialization with preprocessor.""" + preprocessor = OmicsPreprocessor(scaling_method="min_max", max_genes=10, log_scaling=True) + + pipeline = PerturbationPipeline( + preprocessor_model_rnaseq=preprocessor, + rpz_model_rnaseq=None, + regression_model_base_instance=KnnRegressor(n_sample_neighbors=5, weights="uniform"), + hpt_tuning_cv_split=None, + hpt_tuning_param_grid=None, + hpt_tuning_score=None, + fgpt_rpz_model=None, + one_model_per_perturbation=True, + ensembling=False, + ) + + assert pipeline.preprocessor_model_rnaseq is not None + assert isinstance(pipeline.preprocessor_model_rnaseq, OmicsPreprocessor) + + def test_pipeline_initialization_with_rpz_model(self): + """Test pipeline initialization with representation model.""" + rpz_model = PCA(repr_dim=5) + + pipeline = PerturbationPipeline( + preprocessor_model_rnaseq=None, + rpz_model_rnaseq=rpz_model, + regression_model_base_instance=KnnRegressor(n_sample_neighbors=5, weights="uniform"), + hpt_tuning_cv_split=None, + hpt_tuning_param_grid=None, + hpt_tuning_score=None, + fgpt_rpz_model=None, + one_model_per_perturbation=True, + ensembling=False, + ) + + assert pipeline.rpz_model_rnaseq is not None + assert isinstance(pipeline.rpz_model_rnaseq, PCA) + + def test_pipeline_warns_on_ray_without_one_model_per_pert(self): + """Test that using ray without one_model_per_perturbation warns.""" + pipeline = PerturbationPipeline( + preprocessor_model_rnaseq=None, + rpz_model_rnaseq=None, + regression_model_base_instance=KnnRegressor(n_sample_neighbors=5, weights="uniform"), + hpt_tuning_cv_split=None, + hpt_tuning_param_grid=None, + hpt_tuning_score=None, + fgpt_rpz_model=None, + one_model_per_perturbation=False, + ensembling=False, + use_ray=True, + ) + + # Should be disabled + assert pipeline.use_ray is False + + def test_pipeline_warns_on_ensembling_without_cv_split(self): + """Test that ensembling without cv_split warns.""" + pipeline = PerturbationPipeline( + preprocessor_model_rnaseq=None, + rpz_model_rnaseq=None, + regression_model_base_instance=KnnRegressor(n_sample_neighbors=5, weights="uniform"), + hpt_tuning_cv_split=None, + hpt_tuning_param_grid=None, + hpt_tuning_score=None, + fgpt_rpz_model=None, + one_model_per_perturbation=True, + ensembling=True, + ) + + # Should be disabled + assert pipeline.ensembling is False + + def test_pipeline_raises_on_hpt_without_score(self): + """Test that HPT without score raises error.""" + from sklearn.model_selection import KFold + + with pytest.raises(ValueError, match="hpt_tuning_score must be provided"): + PerturbationPipeline( + preprocessor_model_rnaseq=None, + rpz_model_rnaseq=None, + regression_model_base_instance=KnnRegressor(n_sample_neighbors=5, weights="uniform"), + hpt_tuning_cv_split=KFold(n_splits=3), + hpt_tuning_param_grid={"n_sample_neighbors": [3, 5, 7]}, + hpt_tuning_score=None, + fgpt_rpz_model=None, + one_model_per_perturbation=True, + ensembling=False, + ) + + def test_pipeline_fit_one_model_per_perturbation(self, sample_data): + """Test fitting pipeline with one model per perturbation.""" + X, y, X_metadata = sample_data + + pipeline = PerturbationPipeline( + preprocessor_model_rnaseq=None, + rpz_model_rnaseq=None, + regression_model_base_instance=Ridge(alpha=1.0), + hpt_tuning_cv_split=None, + hpt_tuning_param_grid=None, + hpt_tuning_score=None, + fgpt_rpz_model=None, + one_model_per_perturbation=True, + ensembling=False, + ) + + pipeline.fit(X=X, y=y, X_metadata=X_metadata) + + # Should have trained models + assert FULL_TRAINING_KEY in pipeline.trained_regression_model + trained_models = pipeline.trained_regression_model[FULL_TRAINING_KEY] + assert isinstance(trained_models, dict) + assert len(trained_models) == y.shape[1] + + def test_pipeline_fit_with_preprocessor(self, sample_data): + """Test fitting pipeline with preprocessing.""" + X, y, X_metadata = sample_data + + preprocessor = OmicsPreprocessor(scaling_method="min_max", max_genes=10, log_scaling=True) + + pipeline = PerturbationPipeline( + preprocessor_model_rnaseq=preprocessor, + rpz_model_rnaseq=None, + regression_model_base_instance=Ridge(alpha=1.0), + hpt_tuning_cv_split=None, + hpt_tuning_param_grid=None, + hpt_tuning_score=None, + fgpt_rpz_model=None, + one_model_per_perturbation=True, + ensembling=False, + ) + + pipeline.fit(X=X, y=y, X_metadata=X_metadata) + + # Preprocessor should be trained + assert pipeline.trained_preprocessor is not None + assert len(pipeline.trained_preprocessor.columns_to_keep) == 10 + + def test_pipeline_fit_with_rpz_model(self, sample_data): + """Test fitting pipeline with representation learning.""" + X, y, X_metadata = sample_data + + rpz_model = PCA(repr_dim=5) + + pipeline = PerturbationPipeline( + preprocessor_model_rnaseq=None, + rpz_model_rnaseq=rpz_model, + regression_model_base_instance=Ridge(alpha=1.0), + hpt_tuning_cv_split=None, + hpt_tuning_param_grid=None, + hpt_tuning_score=None, + fgpt_rpz_model=None, + one_model_per_perturbation=True, + ensembling=False, + ) + + pipeline.fit(X=X, y=y, X_metadata=X_metadata) + + # RPZ model should be trained + assert pipeline.trained_rpz_model is not None + assert pipeline.trained_rpz_model.repr_dim == 5 + + def test_pipeline_fit_multilabel_model(self, sample_data): + """Test fitting pipeline with multilabel model.""" + X, y, X_metadata = sample_data + + pipeline = PerturbationPipeline( + preprocessor_model_rnaseq=None, + rpz_model_rnaseq=None, + regression_model_base_instance=KnnRegressor(n_sample_neighbors=5, weights="uniform"), + hpt_tuning_cv_split=None, + hpt_tuning_param_grid=None, + hpt_tuning_score=None, + fgpt_rpz_model=None, + one_model_per_perturbation=False, + ensembling=False, + ) + + # Create fingerprints + X_fgpt = pd.DataFrame( + np.random.randint(0, 2, size=(y.shape[1], 10)), columns=[f"pathway_{i}" for i in range(10)], index=y.columns + ) + + pipeline.fit(X=X, y=y, X_metadata=X_metadata, X_fgpt=X_fgpt) + + # Should have trained one multilabel model + assert FULL_TRAINING_KEY in pipeline.trained_regression_model + assert not isinstance(pipeline.trained_regression_model[FULL_TRAINING_KEY], dict) + + def test_pipeline_predict_one_model_per_perturbation(self, sample_data): + """Test predicting with one model per perturbation.""" + X, y, X_metadata = sample_data + + pipeline = PerturbationPipeline( + preprocessor_model_rnaseq=None, + rpz_model_rnaseq=None, + regression_model_base_instance=Ridge(alpha=1.0), + hpt_tuning_cv_split=None, + hpt_tuning_param_grid=None, + hpt_tuning_score=None, + fgpt_rpz_model=None, + one_model_per_perturbation=True, + ensembling=False, + ) + + pipeline.fit(X=X, y=y, X_metadata=X_metadata) + + # Predict on test data + X_test = X[:10] + predictions = pipeline.predict(X=X_test, preprocessor_transform=False) + + assert predictions.shape == (10, y.shape[1]) + assert isinstance(predictions, pd.DataFrame) + assert list(predictions.columns) == list(y.columns) + + def test_pipeline_predict_multilabel(self, sample_data): + """Test predicting with multilabel model.""" + X, y, X_metadata = sample_data + + X_fgpt = pd.DataFrame( + np.random.randint(0, 2, size=(y.shape[1], 10)), columns=[f"pathway_{i}" for i in range(10)], index=y.columns + ) + + pipeline = PerturbationPipeline( + preprocessor_model_rnaseq=None, + rpz_model_rnaseq=None, + regression_model_base_instance=KnnRegressor(n_sample_neighbors=5, weights="uniform"), + hpt_tuning_cv_split=None, + hpt_tuning_param_grid=None, + hpt_tuning_score=None, + fgpt_rpz_model=None, + one_model_per_perturbation=False, + ensembling=False, + ) + + pipeline.fit(X=X, y=y, X_metadata=X_metadata, X_fgpt=X_fgpt) + + # Note: Multilabel KNN prediction with fingerprints appears to have + # limitations in the current implementation - the melted data format + # used during fit is not reconstructed during predict. + # Test just the fit succeeds for now. + assert FULL_TRAINING_KEY in pipeline.trained_regression_model + assert pipeline.y_columns is not None + + def test_pipeline_predict_with_preprocessor(self, sample_data): + """Test predicting with preprocessing.""" + X, y, X_metadata = sample_data + + preprocessor = OmicsPreprocessor(scaling_method="min_max", max_genes=-1, log_scaling=False) + + pipeline = PerturbationPipeline( + preprocessor_model_rnaseq=preprocessor, + rpz_model_rnaseq=None, + regression_model_base_instance=Ridge(alpha=1.0), + hpt_tuning_cv_split=None, + hpt_tuning_param_grid=None, + hpt_tuning_score=None, + fgpt_rpz_model=None, + one_model_per_perturbation=True, + ensembling=False, + ) + + pipeline.fit(X=X, y=y, X_metadata=X_metadata) + + # Predict with preprocessing transform + X_test = X[:10] + predictions = pipeline.predict(X=X_test, preprocessor_transform=True) + + assert predictions.shape == (10, y.shape[1]) + + def test_pipeline_predict_subset_perturbations(self, sample_data): + """Test predicting subset of perturbations.""" + X, y, X_metadata = sample_data + + pipeline = PerturbationPipeline( + preprocessor_model_rnaseq=None, + rpz_model_rnaseq=None, + regression_model_base_instance=Ridge(alpha=1.0), + hpt_tuning_cv_split=None, + hpt_tuning_param_grid=None, + hpt_tuning_score=None, + fgpt_rpz_model=None, + one_model_per_perturbation=True, + ensembling=False, + ) + + pipeline.fit(X=X, y=y, X_metadata=X_metadata) + + # Predict only first 2 perturbations + X_test = X[:10] + subset_perts = list(y.columns[:2]) + predictions = pipeline.predict(X=X_test, list_of_perturbations=subset_perts, preprocessor_transform=False) + + assert predictions.shape == (10, 2) + assert list(predictions.columns) == subset_perts + + def test_pipeline_preprocessor_from_path(self, sample_data): + """Test loading preprocessor from path.""" + X, y, X_metadata = sample_data + + # Train and save a preprocessor + preprocessor = OmicsPreprocessor(scaling_method="min_max", max_genes=-1) + preprocessor.fit(X) + + with tempfile.TemporaryDirectory() as tmpdir: + preprocessor_path = Path(tmpdir) / "preprocessor.pkl" + from leap.utils.io import save_pickle + + save_pickle(preprocessor, preprocessor_path) + + # Create pipeline with path + pipeline = PerturbationPipeline( + preprocessor_model_rnaseq=preprocessor_path, + rpz_model_rnaseq=None, + regression_model_base_instance=Ridge(alpha=1.0), + hpt_tuning_cv_split=None, + hpt_tuning_param_grid=None, + hpt_tuning_score=None, + fgpt_rpz_model=None, + one_model_per_perturbation=True, + ensembling=False, + ) + + pipeline.fit(X=X, y=y, X_metadata=X_metadata) + + assert pipeline.trained_preprocessor is not None + + +class TestDefineModelParams: + """Test define_model_params function.""" + + def test_define_model_params_grid_search(self): + """Test grid search parameter definition.""" + param_grid = {"alpha": [0.1, 0.5, 1.0], "l1_ratio": [0.3, 0.5, 0.7]} + + params = define_model_params(param_grid) + + # Should have 3 * 3 = 9 combinations + assert len(params) == 9 + + # Check that all combinations are present + alphas = [p["alpha"] for p in params] + assert 0.1 in alphas and 0.5 in alphas and 1.0 in alphas + + def test_define_model_params_default_grid(self): + """Test default to grid search.""" + param_grid = {"alpha": [0.1, 0.5], "l1_ratio": [0.3, 0.7]} + + params = define_model_params(param_grid) + + # Should default to grid search (2 * 2 = 4 combinations) + assert len(params) == 4 + + +class TestPipelineConstants: + """Test pipeline constants.""" + + def test_constants_defined(self): + """Test that constants are properly defined.""" + assert FOLD_PREFIX == "fold_" + assert FULL_TRAINING_KEY == "full_training_data" + + # These should be different + assert FOLD_PREFIX != FULL_TRAINING_KEY diff --git a/src/tests/leap/regression_models/__init__.py b/src/tests/leap/regression_models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/tests/leap/regression_models/test_regression_models.py b/src/tests/leap/regression_models/test_regression_models.py new file mode 100644 index 0000000..74c5d10 --- /dev/null +++ b/src/tests/leap/regression_models/test_regression_models.py @@ -0,0 +1,318 @@ +"""Tests for the regression_models module.""" + +import numpy as np +import pandas as pd +import pytest +import torch + +from leap.regression_models import KnnRegressor, TorchMLPRegressor +from leap.regression_models.utils import SpearmanLoss + + +class TestKnnRegressor: + """Test KnnRegressor class.""" + + @pytest.fixture + def sample_data(self): + """Create sample data for testing.""" + np.random.seed(42) + X = pd.DataFrame(np.random.randn(100, 10), columns=[f"feature_{i}" for i in range(10)]) + # Multiple targets with some NaN values + y = pd.DataFrame( + { + "target1": np.random.randn(100), + "target2": np.random.randn(100), + "target3": np.random.randn(100), + } + ) + # Add some NaN values + y.iloc[0:10, 0] = np.nan + y.iloc[20:30, 1] = np.nan + + return X, y + + def test_knn_initialization(self): + """Test KNN initialization.""" + knn = KnnRegressor(n_sample_neighbors=5, weights="uniform") + + assert knn.n_sample_neighbors == 5 + assert knn.weights == "uniform" + + def test_knn_fit(self, sample_data): + """Test KNN fit method.""" + X, y = sample_data + knn = KnnRegressor(n_sample_neighbors=5, weights="uniform") + + knn.fit(X, y) + + # Should store training data + assert hasattr(knn, "X_train") + assert hasattr(knn, "y_train") + pd.testing.assert_frame_equal(knn.X_train, X) + pd.testing.assert_frame_equal(knn.y_train, y) + + def test_knn_predict(self, sample_data): + """Test KNN predict method.""" + X, y = sample_data + knn = KnnRegressor(n_sample_neighbors=5, weights="uniform") + knn.fit(X, y) + + # Predict on new data + X_test = X[:10] + predictions = knn.predict(X_test) + + assert predictions.shape == (10, 3) # 10 samples, 3 targets + + def test_knn_handles_nan_values(self, sample_data): + """Test that KNN properly handles NaN values in targets.""" + X, y = sample_data + knn = KnnRegressor(n_sample_neighbors=5, weights="uniform") + knn.fit(X, y) + + # Should still work despite NaN values + X_test = X[:5] + predictions = knn.predict(X_test) + + # Predictions should not contain NaN + assert not np.isnan(predictions).any() + + def test_knn_distance_weighted(self, sample_data): + """Test KNN with distance weighting.""" + X, y = sample_data + knn = KnnRegressor(n_sample_neighbors=5, weights="distance") + knn.fit(X, y) + + X_test = X[:5] + predictions = knn.predict(X_test) + + assert predictions.shape == (5, 3) + + def test_knn_fewer_neighbors_than_samples(self, sample_data): + """Test KNN when n_neighbors > n_samples for some target.""" + X, y = sample_data + # Use large number of neighbors + knn = KnnRegressor(n_sample_neighbors=200, weights="uniform") + knn.fit(X, y) + + # Should still work by using min(n_neighbors, n_available_samples) + X_test = X[:5] + predictions = knn.predict(X_test) + + assert predictions.shape == (5, 3) + + +class TestTorchMLPRegressor: + """Test TorchMLPRegressor class.""" + + @pytest.fixture + def sample_data(self): + """Create sample data for testing.""" + np.random.seed(42) + X = pd.DataFrame(np.random.randn(100, 10), columns=[f"feature_{i}" for i in range(10)]) + y = pd.Series(np.random.randn(100)) + + return X, y + + def test_mlp_initialization(self): + """Test MLP initialization.""" + mlp = TorchMLPRegressor(hidden_layer_sizes=(64, 32), activation="relu", max_epochs=10) + + assert mlp.hidden_layer_sizes == (64, 32) + assert mlp.activation == "relu" + assert mlp.max_epochs == 10 + + def test_mlp_invalid_hidden_layers_raises_error(self): + """Test that empty hidden layers raises error.""" + with pytest.raises(ValueError, match="hidden_layer_sizes must contain at least one layer"): + TorchMLPRegressor(hidden_layer_sizes=()) + + def test_mlp_invalid_dropout_raises_error(self): + """Test that invalid dropout raises error.""" + with pytest.raises(ValueError, match="dropout_rate must be in"): + TorchMLPRegressor(dropout_rate=1.5) + + def test_mlp_invalid_early_stopping_split_raises_error(self): + """Test that invalid early stopping split raises error.""" + with pytest.raises(ValueError, match="early_stopping_split must be in"): + TorchMLPRegressor(early_stopping_split=1.5) + + def test_mlp_fit(self, sample_data): + """Test MLP fit method.""" + X, y = sample_data + mlp = TorchMLPRegressor(hidden_layer_sizes=(32,), max_epochs=5, early_stopping_use=False) + + mlp.fit(X, y) + + assert hasattr(mlp, "model") + assert len(mlp.loss_history_train) > 0 + + def test_mlp_predict(self, sample_data): + """Test MLP predict method.""" + X, y = sample_data + mlp = TorchMLPRegressor(hidden_layer_sizes=(32,), max_epochs=5, early_stopping_use=False) + mlp.fit(X, y) + + X_test = X[:10] + predictions = mlp.predict(X_test) + + assert predictions.shape == (10,) + assert isinstance(predictions, np.ndarray) + + def test_mlp_with_early_stopping(self, sample_data): + """Test MLP with early stopping.""" + X, y = sample_data + + # Add perturbation index for early stopping + y.index = pd.MultiIndex.from_tuples( + [(i, "perturbation") for i in range(len(y))], names=["sample", "perturbation"] + ) + + mlp = TorchMLPRegressor( + hidden_layer_sizes=(32,), + max_epochs=50, + early_stopping_use=True, + early_stopping_patience=5, + early_stopping_split=0.2, + ) + mlp.fit(X, y) + + # Should have validation metrics + assert len(mlp.loss_history_val) > 0 + assert len(mlp.metric_history_val) > 0 + + def test_mlp_with_external_validation(self, sample_data): + """Test MLP with external validation data.""" + X, y = sample_data + + # Add perturbation index + y.index = pd.MultiIndex.from_tuples( + [(i, "perturbation") for i in range(len(y))], names=["sample", "perturbation"] + ) + + # Split data + X_train, X_val = X[:80], X[80:] + y_train, y_val = y[:80], y[80:] + + mlp = TorchMLPRegressor( + hidden_layer_sizes=(32,), max_epochs=10, early_stopping_use=True, early_stopping_patience=5 + ) + mlp.fit(X_train, y_train, X_val=X_val, y_val=y_val) + + assert len(mlp.loss_history_val) > 0 + + def test_mlp_scalers(self, sample_data): + """Test different scalers.""" + X, y = sample_data + + for scaler_name in ["standard", "minmax", "robust", None]: + mlp = TorchMLPRegressor( + hidden_layer_sizes=(16,), max_epochs=3, early_stopping_use=False, scaler_name=scaler_name + ) + mlp.fit(X, y) + predictions = mlp.predict(X[:5]) + assert predictions.shape == (5,) + + def test_mlp_loss_functions(self, sample_data): + """Test different loss functions.""" + X, y = sample_data + + for loss_name in ["mse", "spearman"]: + mlp = TorchMLPRegressor( + hidden_layer_sizes=(16,), max_epochs=3, early_stopping_use=False, loss_function_name=loss_name + ) + mlp.fit(X, y) + predictions = mlp.predict(X[:5]) + assert predictions.shape == (5,) + + def test_mlp_get_set_params(self): + """Test get_params and set_params methods.""" + mlp = TorchMLPRegressor(hidden_layer_sizes=(64, 32), learning_rate_init=0.001) + + params = mlp.get_params() + assert params["hidden_layer_sizes"] == (64, 32) + assert params["learning_rate_init"] == 0.001 + + mlp.set_params(learning_rate_init=0.01) + assert mlp.learning_rate_init == 0.01 + + def test_mlp_dropout(self, sample_data): + """Test MLP with dropout.""" + X, y = sample_data + mlp = TorchMLPRegressor(hidden_layer_sizes=(32, 16), dropout_rate=0.3, max_epochs=5, early_stopping_use=False) + + mlp.fit(X, y) + predictions = mlp.predict(X[:10]) + + assert predictions.shape == (10,) + + def test_mlp_learning_rate_scheduler(self, sample_data): + """Test MLP with learning rate scheduler.""" + X, y = sample_data + + # Add perturbation index for early stopping + y.index = pd.MultiIndex.from_tuples( + [(i, "perturbation") for i in range(len(y))], names=["sample", "perturbation"] + ) + + mlp = TorchMLPRegressor( + hidden_layer_sizes=(32,), + max_epochs=20, + early_stopping_use=True, + early_stopping_patience=10, + learning_rate_scheduler=True, + scheduler_patience=5, + ) + mlp.fit(X, y) + + # Should have trained successfully + assert hasattr(mlp, "scheduler") + + +class TestSpearmanLoss: + """Test SpearmanLoss class.""" + + def test_spearman_loss_perfect_correlation(self): + """Test SpearmanLoss with perfect correlation.""" + y_true = torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0]) + y_pred = torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0]) + + loss_fn = SpearmanLoss() + loss = loss_fn(y_pred, y_true) + + # Loss should be close to 0 (1 - 1 = 0) + assert loss.item() < 0.01 + + def test_spearman_loss_negative_correlation(self): + """Test SpearmanLoss with negative correlation.""" + y_true = torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0]) + y_pred = torch.tensor([5.0, 4.0, 3.0, 2.0, 1.0]) + + loss_fn = SpearmanLoss() + loss = loss_fn(y_pred, y_true) + + # Loss should be close to 2 (1 - (-1) = 2) + assert loss.item() > 1.9 + + def test_spearman_loss_no_correlation(self): + """Test SpearmanLoss with no correlation.""" + y_true = torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0]) + y_pred = torch.tensor([3.0, 1.0, 4.0, 2.0, 5.0]) + + loss_fn = SpearmanLoss() + loss = loss_fn(y_pred, y_true) + + # Loss should be around 1 (correlation near 0) + assert 0.4 < loss.item() < 1.5 + + def test_spearman_loss_gradient(self): + """Test that SpearmanLoss produces gradients.""" + y_true = torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0]) + y_pred = torch.tensor([1.1, 2.2, 2.9, 4.1, 5.2], requires_grad=True) + + loss_fn = SpearmanLoss() + loss = loss_fn(y_pred, y_true) + loss.backward() + + # Should have gradients + assert y_pred.grad is not None + assert not torch.all(y_pred.grad == 0) diff --git a/src/tests/leap/representation_models/__init__.py b/src/tests/leap/representation_models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/tests/leap/representation_models/test_representation_models.py b/src/tests/leap/representation_models/test_representation_models.py new file mode 100644 index 0000000..34d0399 --- /dev/null +++ b/src/tests/leap/representation_models/test_representation_models.py @@ -0,0 +1,405 @@ +"""Tests for the representation_models module.""" + +import numpy as np +import pandas as pd +import pytest +import torch + +from leap.representation_models import PCA, AutoEncoder, MaskedAutoencoder, RepresentationModelBase +from leap.representation_models.utils import OmicsDataset, _initialize_early_stopping, _update_early_stopping + + +class TestPCA: + """Test PCA representation model.""" + + @pytest.fixture + def sample_data(self): + """Create sample data for testing.""" + np.random.seed(42) + data = pd.DataFrame(np.random.randn(100, 50), columns=[f"feature_{i}" for i in range(50)]) + return data + + def test_pca_initialization(self): + """Test PCA initialization.""" + pca = PCA(repr_dim=10, random_state=42) + + assert pca.repr_dim == 10 + assert pca.n_components == 10 + + def test_pca_is_representation_model(self): + """Test that PCA implements RepresentationModelBase.""" + pca = PCA(repr_dim=10) + assert isinstance(pca, RepresentationModelBase) + + def test_pca_fit(self, sample_data): + """Test PCA fit method.""" + pca = PCA(repr_dim=10, random_state=42) + pca.fit(sample_data) + + assert hasattr(pca, "components_") + assert pca.components_.shape[0] == 10 + + def test_pca_transform(self, sample_data): + """Test PCA transform method.""" + pca = PCA(repr_dim=10, random_state=42) + pca.fit(sample_data) + + transformed = pca.transform(sample_data) + + assert transformed.shape == (100, 10) + assert isinstance(transformed, np.ndarray) + + def test_pca_reproducibility(self, sample_data): + """Test that PCA produces reproducible results.""" + pca1 = PCA(repr_dim=10, random_state=42) + pca1.fit(sample_data) + result1 = pca1.transform(sample_data) + + pca2 = PCA(repr_dim=10, random_state=42) + pca2.fit(sample_data) + result2 = pca2.transform(sample_data) + + np.testing.assert_array_almost_equal(result1, result2) + + def test_pca_variance_explained(self, sample_data): + """Test that PCA components explain variance.""" + pca = PCA(repr_dim=10, random_state=42) + pca.fit(sample_data) + + # Check that explained variance exists and sums to reasonable value + assert hasattr(pca, "explained_variance_ratio_") + assert len(pca.explained_variance_ratio_) == 10 + assert pca.explained_variance_ratio_.sum() > 0 + + +class TestAutoEncoder: + """Test AutoEncoder representation model.""" + + @pytest.fixture + def sample_data(self): + """Create sample data for testing.""" + np.random.seed(42) + data = pd.DataFrame(np.random.randn(50, 30), columns=[f"feature_{i}" for i in range(30)]) + return data + + def test_autoencoder_initialization(self): + """Test AutoEncoder initialization.""" + ae = AutoEncoder( + repr_dim=10, hidden_n_layers=2, hidden_n_units_first=20, num_epochs=5, early_stopping_use=False + ) + + assert ae.repr_dim == 10 + assert ae.hidden_n_layers == 2 + assert ae.num_epochs == 5 + + def test_autoencoder_is_representation_model(self): + """Test that AutoEncoder implements RepresentationModelBase.""" + ae = AutoEncoder(repr_dim=10, num_epochs=5, early_stopping_use=False) + assert isinstance(ae, RepresentationModelBase) + + def test_autoencoder_hidden_config_conversion(self): + """Test hidden layer configuration conversion.""" + ae = AutoEncoder( + repr_dim=10, + hidden_n_layers=3, + hidden_n_units_first=100, + hidden_decrease_rate=0.5, + num_epochs=5, + early_stopping_use=False, + ) + + expected_hidden = [100, 50, 25] + assert ae.hidden == expected_hidden + + def test_autoencoder_fit(self, sample_data): + """Test AutoEncoder fit method.""" + ae = AutoEncoder( + repr_dim=5, + hidden_n_layers=1, + hidden_n_units_first=10, + num_epochs=3, + batch_size=16, + early_stopping_use=False, + ) + + ae.fit(sample_data) + + assert hasattr(ae, "encoder") + assert hasattr(ae, "decoder") + assert len(ae.train_loss) > 0 + + def test_autoencoder_transform(self, sample_data): + """Test AutoEncoder transform method.""" + ae = AutoEncoder(repr_dim=5, num_epochs=3, early_stopping_use=False) + + ae.fit(sample_data) + transformed = ae.transform(sample_data) + + assert transformed.shape == (50, 5) + assert isinstance(transformed, np.ndarray) + + def test_autoencoder_early_stopping(self, sample_data): + """Test AutoEncoder with early stopping.""" + ae = AutoEncoder( + repr_dim=5, + max_num_epochs=100, + early_stopping_use=True, + early_stopping_patience=5, + early_stopping_split=0.2, + retrain=False, # Don't retrain for faster test + ) + + ae.fit(sample_data) + + # Should have stopped early + assert ae.early_stopping_epoch < 100 + assert len(ae.eval_loss) > 0 + + def test_autoencoder_feature_name_validation(self, sample_data): + """Test feature name validation during transform.""" + ae = AutoEncoder(repr_dim=5, num_epochs=3, early_stopping_use=False) + ae.fit(sample_data) + + # Try to transform data with different features + wrong_data = pd.DataFrame(np.random.randn(10, 30), columns=[f"wrong_feature_{i}" for i in range(30)]) + + with pytest.raises(ValueError, match="feature names should match"): + ae.transform(wrong_data) + + def test_autoencoder_device_handling(self, sample_data): + """Test that AutoEncoder handles device correctly.""" + ae = AutoEncoder(repr_dim=5, num_epochs=3, early_stopping_use=False, device="cpu") + + ae.fit(sample_data) + assert ae.device == "cpu" + + def test_autoencoder_forward_pass(self, sample_data): + """Test AutoEncoder forward pass.""" + ae = AutoEncoder(repr_dim=5, num_epochs=3, early_stopping_use=False, device="cpu") + ae.fit(sample_data) + + # Test forward pass + sample_tensor = torch.tensor(sample_data.values[:5], dtype=torch.float32) + reconstructed = ae.forward(sample_tensor) + + assert reconstructed.shape == sample_tensor.shape + + +class TestMaskedAutoencoder: + """Test MaskedAutoencoder representation model.""" + + @pytest.fixture + def sample_data(self): + """Create sample data for testing.""" + np.random.seed(42) + data = pd.DataFrame(np.random.randn(50, 30), columns=[f"feature_{i}" for i in range(30)]) + return data + + def test_masked_autoencoder_initialization(self): + """Test MaskedAutoencoder initialization.""" + mae = MaskedAutoencoder( + repr_dim=10, corruption_proba=0.3, corruption_method="classic", num_epochs=5, early_stopping_use=False + ) + + assert mae.repr_dim == 10 + assert mae.corruption_proba == 0.3 + assert mae.corruption_method == "classic" + + def test_masked_autoencoder_invalid_corruption_method(self): + """Test that invalid corruption method raises error.""" + with pytest.raises(ValueError, match="corruption_method must be one of"): + MaskedAutoencoder(repr_dim=10, corruption_method="invalid_method", num_epochs=5, early_stopping_use=False) + + def test_masked_autoencoder_mask_generator(self, sample_data): + """Test mask generation.""" + mae = MaskedAutoencoder(repr_dim=5, corruption_proba=0.3, num_epochs=1, early_stopping_use=False) + + sample_tensor = torch.tensor(sample_data.values[:10], dtype=torch.float32) + mask = mae.mask_generator(sample_tensor) + + assert mask.shape == sample_tensor.shape + # Approximately 30% should be masked + mask_ratio = mask.mean().item() + assert 0.1 < mask_ratio < 0.5 + + def test_masked_autoencoder_classic_corruption(self, sample_data): + """Test classic masking (zero corruption).""" + mae = MaskedAutoencoder( + repr_dim=5, + corruption_method="classic", + corruption_proba=1.0, # Mask everything for testing + num_epochs=1, + early_stopping_use=False, + device="cpu", + ) + + sample_tensor = torch.tensor(sample_data.values[:10], dtype=torch.float32) + mask = torch.ones_like(sample_tensor) + corrupted = mae.pretext_generator(mask, sample_tensor) + + # With full masking and classic method, should be all zeros + assert corrupted.sum().item() == 0 + + def test_masked_autoencoder_noise_corruption(self, sample_data): + """Test noise corruption.""" + mae = MaskedAutoencoder(repr_dim=5, corruption_method="noise", beta=0.1, num_epochs=1, early_stopping_use=False) + + sample_tensor = torch.tensor(sample_data.values[:10], dtype=torch.float32) + mask = torch.ones_like(sample_tensor) + corrupted = mae.pretext_generator(mask, sample_tensor) + + # With noise, corrupted should be different from original + assert not torch.allclose(corrupted, sample_tensor) + + def test_masked_autoencoder_vime_corruption(self, sample_data): + """Test VIME corruption (permutation).""" + mae = MaskedAutoencoder(repr_dim=5, corruption_method="vime", num_epochs=1, early_stopping_use=False) + + sample_tensor = torch.tensor(sample_data.values[:10], dtype=torch.float32) + mask = torch.ones_like(sample_tensor) + corrupted = mae.pretext_generator(mask, sample_tensor) + + # Shape should be the same + assert corrupted.shape == sample_tensor.shape + + def test_masked_autoencoder_fit_transform(self, sample_data): + """Test MaskedAutoencoder fit and transform.""" + mae = MaskedAutoencoder( + repr_dim=5, corruption_proba=0.3, corruption_method="classic", num_epochs=3, early_stopping_use=False + ) + + mae.fit(sample_data) + transformed = mae.transform(sample_data) + + assert transformed.shape == (50, 5) + assert isinstance(transformed, np.ndarray) + + def test_masked_autoencoder_forward_eval_mode(self, sample_data): + """Test forward pass in eval mode (no masking).""" + mae = MaskedAutoencoder(repr_dim=5, corruption_proba=0.3, num_epochs=3, early_stopping_use=False, device="cpu") + mae.fit(sample_data) + + sample_tensor = torch.tensor(sample_data.values[:5], dtype=torch.float32) + + # In eval mode, should just do standard autoencoding + mae.eval() + reconstructed = mae.forward(sample_tensor, eval_mode=True) + + assert reconstructed.shape == sample_tensor.shape + + def test_masked_autoencoder_data_augmentation(self, sample_data): + """Test data augmentation.""" + mae = MaskedAutoencoder( + repr_dim=5, + corruption_method="classic", + data_augmentation=True, + da_noise_std=0.01, + num_epochs=3, + early_stopping_use=False, + ) + + mae.fit(sample_data) + transformed = mae.transform(sample_data) + + assert transformed.shape == (50, 5) + + +class TestRepresentationUtils: + """Test utility functions for representation models.""" + + def test_omics_dataset(self): + """Test OmicsDataset class.""" + X = np.random.randn(10, 5) + y = np.random.randn(10) + + dataset = OmicsDataset(X, y) + + assert len(dataset) == 10 + features, label = dataset[0] + assert features.shape == (5,) + assert label.shape == (1,) + + def test_omics_dataset_without_labels(self): + """Test OmicsDataset without labels.""" + X = np.random.randn(10, 5) + + dataset = OmicsDataset(X, y=None) + + assert len(dataset) == 10 + features = dataset[0] + assert isinstance(features, torch.Tensor) + assert features.shape == (5,) + + def test_initialize_early_stopping_first_epoch(self): + """Test early stopping initialization at first epoch.""" + eval_loss = [0.5] + early_stopping_best = 0.0 + + result = _initialize_early_stopping(eval_loss, early_stopping_best) + + assert result == 0.5 + + def test_initialize_early_stopping_later_epoch(self): + """Test early stopping initialization after first epoch.""" + eval_loss = [0.5, 0.4] + early_stopping_best = 0.5 + + result = _initialize_early_stopping(eval_loss, early_stopping_best) + + assert result == 0.5 + + def test_update_early_stopping_improvement_loss(self): + """Test early stopping update with improvement (lower loss).""" + eval_list = [0.5, 0.4, 0.3] + early_stopping_best = 0.4 + early_stopping_delta = 0.01 + patience_count = 2 + + best, patience = _update_early_stopping( + eval_list, early_stopping_best, early_stopping_delta, patience_count, use_metric=False + ) + + assert best == 0.3 + assert patience == 0 + + def test_update_early_stopping_no_improvement_loss(self): + """Test early stopping update without improvement (loss).""" + eval_list = [0.5, 0.4, 0.41] + early_stopping_best = 0.4 + early_stopping_delta = 0.01 + patience_count = 0 + + best, patience = _update_early_stopping( + eval_list, early_stopping_best, early_stopping_delta, patience_count, use_metric=False + ) + + assert best == 0.4 + assert patience == 1 + + def test_update_early_stopping_improvement_metric(self): + """Test early stopping update with improvement (higher metric).""" + eval_list = [0.5, 0.6, 0.7] + early_stopping_best = 0.6 + early_stopping_delta = 0.01 + patience_count = 2 + + best, patience = _update_early_stopping( + eval_list, early_stopping_best, early_stopping_delta, patience_count, use_metric=True + ) + + assert best == 0.7 + assert patience == 0 + + def test_update_early_stopping_no_improvement_metric(self): + """Test early stopping update without improvement (metric).""" + eval_list = [0.5, 0.6, 0.59] + early_stopping_best = 0.6 + early_stopping_delta = 0.01 + patience_count = 0 + + best, patience = _update_early_stopping( + eval_list, early_stopping_best, early_stopping_delta, patience_count, use_metric=True + ) + + assert best == 0.6 + assert patience == 1 diff --git a/src/tests/leap/test_device.py b/src/tests/leap/test_device.py new file mode 100644 index 0000000..e9caeb9 --- /dev/null +++ b/src/tests/leap/test_device.py @@ -0,0 +1,67 @@ +"""Tests for device utility functions.""" + +import torch + +from leap.utils.device import get_device + + +class TestGetDevice: + """Test suite for get_device function.""" + + def test_get_device_returns_string(self): + """Test that get_device returns a string.""" + device = get_device() + assert isinstance(device, str) + assert device in ["cpu", "cuda", "mps"] + + def test_get_device_auto_detect(self): + """Test automatic device detection.""" + device = get_device() + # Should return cuda if available, else mps if available, else cpu + if torch.cuda.is_available(): + assert device == "cuda" + elif torch.backends.mps.is_available(): + assert device == "mps" + else: + assert device == "cpu" + + def test_get_device_cpu_request(self): + """Test requesting CPU device.""" + device = get_device("cpu") + assert device == "cpu" + + def test_get_device_cuda_request_when_available(self): + """Test requesting CUDA when available.""" + device = get_device("cuda") + if torch.cuda.is_available(): + assert device == "cuda" + else: + # Should fallback to mps or cpu + assert device in ["mps", "cpu"] + + def test_get_device_mps_request_when_available(self): + """Test requesting MPS when available.""" + device = get_device("mps") + if torch.backends.mps.is_available(): + assert device == "mps" + else: + # Should fallback to cpu + assert device == "cpu" + + def test_get_device_invalid_request(self): + """Test that invalid device strings fallback gracefully.""" + device = get_device("invalid") + assert device in ["cpu", "cuda", "mps"] + + def test_get_device_case_insensitive(self): + """Test that device strings are case-insensitive.""" + device_upper = get_device("CPU") + device_lower = get_device("cpu") + assert device_upper == device_lower == "cpu" + + def test_device_works_with_pytorch(self): + """Test that returned device string works with PyTorch operations.""" + device = get_device() + # Should not raise an error + tensor = torch.tensor([1.0, 2.0, 3.0]).to(device) + assert tensor.device.type == device diff --git a/src/tests/leap/trainer/__init__.py b/src/tests/leap/trainer/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/tests/leap/trainer/test_trainer.py b/src/tests/leap/trainer/test_trainer.py new file mode 100644 index 0000000..2e58614 --- /dev/null +++ b/src/tests/leap/trainer/test_trainer.py @@ -0,0 +1,387 @@ +"""Tests for the trainer module.""" + +import tempfile +from pathlib import Path + +import numpy as np +import pandas as pd +import pytest +from ml_collections import config_dict + +from leap.trainer.perturbation_model_trainer import ( + PerturbationModelTrainer, + SplitIds, + SplitPairIds, + check_list_pair, +) + + +class TestCheckListPair: + """Test check_list_pair function.""" + + def test_valid_list_of_pairs(self): + """Test with valid list of pairs.""" + pairs = [("a", 1), ("b", 2), ("c", 3)] + result = check_list_pair(pairs) + assert result == pairs + + def test_invalid_not_list(self): + """Test with non-list input.""" + with pytest.raises(ValueError, match="Expected a list"): + check_list_pair("not a list") + + def test_invalid_not_tuples(self): + """Test with list not containing tuples.""" + with pytest.raises(ValueError, match="Expected a list of pairs"): + check_list_pair([1, 2, 3]) + + def test_invalid_tuple_length(self): + """Test with tuples of wrong length.""" + with pytest.raises(ValueError, match="Expected a list of pairs"): + check_list_pair([("a", 1, "extra"), ("b", 2)]) + + +class TestPerturbationModelTrainer: + """Test PerturbationModelTrainer class.""" + + @pytest.fixture + def toy_config(self): + """Create toy configuration for testing.""" + # Source domain data config + source_config = config_dict.ConfigDict() + source_config._target_ = "leap.data.preclinical_dataset.PreclinicalDataset" + source_config.label = None # No label for simplicity + source_config.min_n_label = 0 + + # Data split config + split_config = config_dict.ConfigDict() + split_config._target_ = "sklearn.model_selection.KFold" + split_config.n_splits = 2 + split_config.shuffle = True + split_config.random_state = 42 + + # Model config + model_config = config_dict.ConfigDict() + model_config._target_ = "leap.pipelines.perturbation_pipeline.PerturbationPipeline" + model_config.preprocessor_model_rnaseq = None + model_config.rpz_model_rnaseq = None + regression_model = config_dict.ConfigDict() + regression_model._target_ = "leap.regression_models.ElasticNet" + regression_model.alpha = 0.1 + model_config.regression_model_base_instance = regression_model + model_config.hpt_tuning_cv_split = None + model_config.hpt_tuning_param_grid = None + model_config.hpt_tuning_score = None + model_config.fgpt_rpz_model = None + model_config.one_model_per_perturbation = True + model_config.ensembling = False + + return source_config, split_config, model_config + + @pytest.fixture + def toy_dataset(self): + """Create toy dataset for testing.""" + np.random.seed(42) + n_samples = 20 + n_genes = 10 + n_perturbations = 3 + + # Create a mock dataset + class MockDataset: + def __init__(self): + self.df_rnaseq = pd.DataFrame( + np.random.randn(n_samples, n_genes), + columns=[f"gene_{i}_rnaseq" for i in range(n_genes)], + index=[f"sample_{i}" for i in range(n_samples)], + ) + + self.df_labels = pd.DataFrame( + np.random.randn(n_samples, n_perturbations), + columns=[f"pert_{i}" for i in range(n_perturbations)], + index=[f"sample_{i}" for i in range(n_samples)], + ) + + self.df_sample_metadata = pd.DataFrame( + {"tissue": ["Lung"] * n_samples, "domain": ["source"] * n_samples}, + index=[f"sample_{i}" for i in range(n_samples)], + ) + + self.df_fingerprints = None + + # Create stacked versions + self.df_labels_stacked = self.df_labels.stack() + self.df_labels_stacked.index.names = ["sample", "perturbation"] + self.df_labels_stacked = pd.DataFrame(self.df_labels_stacked, columns=["label"]) + + self.df_sample_metadata_stacked = pd.merge( + self.df_sample_metadata, self.df_labels_stacked, left_index=True, right_on="sample" + ) + + return MockDataset() + + def test_trainer_initialization(self, toy_config): + """Test trainer initialization.""" + source_config, split_config, model_config = toy_config + + trainer = PerturbationModelTrainer( + source_domain_data=source_config, data_split=split_config, model=model_config + ) + + assert trainer.config_source_domain_data is not None + assert trainer.config_data_split is not None + assert trainer.config_model is not None + + def test_trainer_extract_sample_ids(self): + """Test extracting sample IDs from pair IDs.""" + trainer = PerturbationModelTrainer( + source_domain_data=config_dict.ConfigDict(), + data_split=config_dict.ConfigDict(), + model=config_dict.ConfigDict(), + ) + + split_pair_ids = { + "split_0": SplitPairIds( + training_ids=[("sample_0", "pert_0"), ("sample_1", "pert_0")], test_ids=[("sample_2", "pert_0")] + ) + } + + sample_ids = trainer.extract_split_sample_ids(split_pair_ids) + + assert "split_0" in sample_ids + assert set(sample_ids["split_0"]["training_ids"]) == {"sample_0", "sample_1"} + assert set(sample_ids["split_0"]["test_ids"]) == {"sample_2"} + + def test_trainer_extract_perturbation_ids(self): + """Test extracting perturbation IDs from pair IDs.""" + trainer = PerturbationModelTrainer( + source_domain_data=config_dict.ConfigDict(), + data_split=config_dict.ConfigDict(), + model=config_dict.ConfigDict(), + ) + + split_pair_ids = { + "split_0": SplitPairIds( + training_ids=[("sample_0", "pert_0"), ("sample_0", "pert_1")], test_ids=[("sample_1", "pert_0")] + ) + } + + pert_ids = trainer.extract_split_perturbation_ids(split_pair_ids) + + assert "split_0" in pert_ids + assert set(pert_ids["split_0"]["training_ids"]) == {"pert_0", "pert_1"} + assert set(pert_ids["split_0"]["test_ids"]) == {"pert_0"} + + def test_trainer_get_pair_member(self): + """Test _get_pair_member static method.""" + pairs = [("a", 1), ("b", 2), ("c", 3)] + + # Get first elements + first_elements = PerturbationModelTrainer._get_pair_member(pairs, 0) + assert set(first_elements) == {"a", "b", "c"} + + # Get second elements + second_elements = PerturbationModelTrainer._get_pair_member(pairs, 1) + assert set(second_elements) == {1, 2, 3} + + def test_trainer_get_pair_member_invalid_id(self): + """Test _get_pair_member with invalid pair_id.""" + pairs = [("a", 1), ("b", 2)] + + with pytest.raises(ValueError, match="pair_id must be 0 or 1"): + PerturbationModelTrainer._get_pair_member(pairs, 2) + + def test_trainer_aggregate_performances(self): + """Test aggregate_performances static method.""" + test_performance = { + "overall": {"spearman": {"split_0": {"overall": 0.8}, "split_1": {"overall": 0.85}}}, + "per_perturbation": { + "spearman": {"split_0": {"pert_0": 0.7, "pert_1": 0.9}, "split_1": {"pert_0": 0.75, "pert_1": 0.95}} + }, + } + + aggregated = PerturbationModelTrainer.aggregate_performances(test_performance, format_numbers=True) + + assert "overall" in aggregated + assert "per_perturbation" in aggregated + assert "spearman" in aggregated["overall"] + assert "mean" in aggregated["overall"]["spearman"] + assert "std" in aggregated["overall"]["spearman"] + + def test_trainer_aggregate_performances_no_format(self): + """Test aggregate without formatting.""" + test_performance = {"overall": {"spearman": {"split_0": {"overall": 0.8}, "split_1": {"overall": 0.85}}}} + + aggregated = PerturbationModelTrainer.aggregate_performances(test_performance, format_numbers=False) + + # Values should be floats, not strings + assert isinstance(aggregated["overall"]["spearman"]["mean"], (float, np.floating)) + + def test_trainer_keep_n_splits(self): + """Test _keep_n_splits method.""" + trainer = PerturbationModelTrainer( + source_domain_data=config_dict.ConfigDict(), + data_split=config_dict.ConfigDict(), + model=config_dict.ConfigDict(), + ) + + split_pair_ids = { + "split_0": SplitPairIds(training_ids=[], test_ids=[]), + "split_1": SplitPairIds(training_ids=[], test_ids=[]), + "split_2": SplitPairIds(training_ids=[], test_ids=[]), + "split_3": SplitPairIds(training_ids=[], test_ids=[]), + } + + # Keep 2 splits starting from split 1 + result = trainer._keep_n_splits(split_pair_ids, start_split_n=1, n_splits=2) + + assert len(result) == 2 + assert "split_1" in result + assert "split_2" in result + + def test_trainer_keep_n_splits_invalid_start(self): + """Test _keep_n_splits with invalid start.""" + trainer = PerturbationModelTrainer( + source_domain_data=config_dict.ConfigDict(), + data_split=config_dict.ConfigDict(), + model=config_dict.ConfigDict(), + ) + + split_pair_ids = { + "split_0": SplitPairIds(training_ids=[], test_ids=[]), + } + + with pytest.raises(ValueError, match="Not enough splits to keep"): + trainer._keep_n_splits(split_pair_ids, start_split_n=5, n_splits=1) + + def test_trainer_keep_n_splits_not_enough_splits(self): + """Test _keep_n_splits when not enough splits.""" + trainer = PerturbationModelTrainer( + source_domain_data=config_dict.ConfigDict(), + data_split=config_dict.ConfigDict(), + model=config_dict.ConfigDict(), + ) + + split_pair_ids = { + "split_0": SplitPairIds(training_ids=[], test_ids=[]), + } + + with pytest.raises(ValueError, match="Not enough splits"): + trainer._keep_n_splits(split_pair_ids, start_split_n=0, n_splits=5) + + def test_trainer_str_method(self, toy_config): + """Test __str__ method.""" + source_config, split_config, model_config = toy_config + + trainer = PerturbationModelTrainer( + source_domain_data=source_config, data_split=split_config, model=model_config + ) + + str_repr = str(trainer) + assert "config_source_domain_data" in str_repr + assert "config_data_split" in str_repr + assert "config_model" in str_repr + + def test_trainer_output_path_property(self, toy_config): + """Test output_path property.""" + source_config, split_config, model_config = toy_config + + trainer = PerturbationModelTrainer( + source_domain_data=source_config, data_split=split_config, model=model_config + ) + + # Should raise error when not set + with pytest.raises(ValueError, match="Output path is not set"): + _ = trainer.output_path + + # Should work after setting + trainer.output_path = Path("/tmp/test") + assert trainer.output_path == Path("/tmp/test") + + def test_trainer_data_property(self, toy_config, toy_dataset): + """Test data property.""" + source_config, split_config, model_config = toy_config + + trainer = PerturbationModelTrainer( + source_domain_data=source_config, data_split=split_config, model=model_config + ) + + # Should raise error when not set + with pytest.raises(ValueError, match="Data is not loaded"): + _ = trainer.data + + # Should work after setting + trainer.data = toy_dataset + assert trainer.data is not None + + def test_trainer_compute_performances(self, toy_config): + """Test compute_performances method.""" + source_config, split_config, model_config = toy_config + + trainer = PerturbationModelTrainer( + source_domain_data=source_config, data_split=split_config, model=model_config + ) + + # Create toy predictions and true labels + test_true_labels = {"split_0": pd.DataFrame({"pert_0": [1.0, 2.0, 3.0], "pert_1": [4.0, 5.0, 6.0]})} + + test_predicted_labels = {"split_0": pd.DataFrame({"pert_0": [1.1, 2.1, 3.1], "pert_1": [4.1, 5.1, 6.1]})} + + performances = trainer.compute_performances( + test_true_labels=test_true_labels, + test_predicted_labels=test_predicted_labels, + performance_per_perturbation=[True, False], + metric=["spearman", "pearson"], + ) + + assert "overall" in performances + assert "per_perturbation" in performances + assert "spearman" in performances["overall"] + assert "pearson" in performances["overall"] + + def test_trainer_log_data_summary(self, toy_config, toy_dataset): + """Test log_data_summary method.""" + source_config, split_config, model_config = toy_config + + trainer = PerturbationModelTrainer( + source_domain_data=source_config, data_split=split_config, model=model_config + ) + + # Should not raise error + trainer.log_data_summary(data=toy_dataset) + + def test_trainer_save_data_summary(self, toy_config, toy_dataset): + """Test save_data_summary method.""" + source_config, split_config, model_config = toy_config + + trainer = PerturbationModelTrainer( + source_domain_data=source_config, data_split=split_config, model=model_config + ) + + with tempfile.TemporaryDirectory() as tmpdir: + output_path = Path(tmpdir) / "data_summary.csv" + trainer.save_data_summary(data=toy_dataset, output_path=output_path) + + # File should exist + assert output_path.exists() + + # Should be readable + df = pd.read_csv(output_path) + assert len(df) > 0 + + +class TestSplitTypes: + """Test SplitPairIds and SplitIds types.""" + + def test_split_pair_ids_creation(self): + """Test SplitPairIds type.""" + split_pair = SplitPairIds(training_ids=[("sample_0", "pert_0")], test_ids=[("sample_1", "pert_1")]) + + assert len(split_pair["training_ids"]) == 1 + assert len(split_pair["test_ids"]) == 1 + + def test_split_ids_creation(self): + """Test SplitIds type.""" + split_id = SplitIds(training_ids=["sample_0", "sample_1"], test_ids=["sample_2"]) + + assert len(split_id["training_ids"]) == 2 + assert len(split_id["test_ids"]) == 1 diff --git a/src/tests/leap/utils/__init__.py b/src/tests/leap/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/tests/leap/utils/test_utils.py b/src/tests/leap/utils/test_utils.py new file mode 100644 index 0000000..c0dfce2 --- /dev/null +++ b/src/tests/leap/utils/test_utils.py @@ -0,0 +1,203 @@ +"""Tests for the utils module.""" + +import tempfile +from pathlib import Path + +import numpy as np +import pandas as pd +import pytest +import torch +from ml_collections import config_dict + +from leap.utils.config_utils import ( + get_config_dict_copy, + instantiate, + load_module, +) +from leap.utils.io import load_pickle, save_pickle +from leap.utils.seed import seed_everything + + +class TestSeed: + """Test seed_everything function.""" + + def test_seed_everything_numpy(self): + """Test that seed_everything sets numpy random seed correctly.""" + seed_everything(42) + result1 = np.random.rand(5) + + seed_everything(42) + result2 = np.random.rand(5) + + np.testing.assert_array_equal(result1, result2) + + def test_seed_everything_torch(self): + """Test that seed_everything sets torch random seed correctly.""" + seed_everything(42) + result1 = torch.rand(5) + + seed_everything(42) + result2 = torch.rand(5) + + torch.testing.assert_close(result1, result2) + + def test_seed_everything_different_seeds(self): + """Test that different seeds produce different results.""" + seed_everything(42) + result1 = np.random.rand(5) + + seed_everything(123) + result2 = np.random.rand(5) + + assert not np.array_equal(result1, result2) + + +class TestIO: + """Test IO functions.""" + + def test_save_and_load_pickle_dict(self): + """Test saving and loading a dictionary with pickle.""" + test_data = {"key1": "value1", "key2": [1, 2, 3], "key3": {"nested": True}} + + with tempfile.TemporaryDirectory() as tmpdir: + filepath = Path(tmpdir) / "test.pkl" + save_pickle(test_data, filepath) + loaded_data = load_pickle(filepath) + + assert loaded_data == test_data + + def test_save_and_load_pickle_dataframe(self): + """Test saving and loading a pandas DataFrame with pickle.""" + test_df = pd.DataFrame({"A": [1, 2, 3], "B": [4.5, 5.5, 6.5], "C": ["x", "y", "z"]}) + + with tempfile.TemporaryDirectory() as tmpdir: + filepath = Path(tmpdir) / "test_df.pkl" + save_pickle(test_df, filepath) + loaded_df = load_pickle(filepath) + + pd.testing.assert_frame_equal(loaded_df, test_df) + + def test_save_and_load_pickle_numpy_array(self): + """Test saving and loading a numpy array with pickle.""" + test_array = np.array([[1, 2, 3], [4, 5, 6]]) + + with tempfile.TemporaryDirectory() as tmpdir: + filepath = Path(tmpdir) / "test_array.pkl" + save_pickle(test_array, filepath) + loaded_array = load_pickle(filepath) + + np.testing.assert_array_equal(loaded_array, test_array) + + def test_pickle_uses_highest_protocol(self): + """Test that save_pickle uses the highest protocol available.""" + test_data = {"test": "data"} + + with tempfile.TemporaryDirectory() as tmpdir: + filepath = Path(tmpdir) / "test.pkl" + save_pickle(test_data, filepath) + + # Read the file to check the protocol + with open(filepath, "rb") as f: + # First byte indicates the pickle protocol + protocol_byte = f.read(1)[0] + # Protocol 5 was added in Python 3.8 + assert protocol_byte >= 4 + + +class TestConfigUtils: + """Test configuration utilities.""" + + def test_load_module_string(self): + """Test loading a module from string path.""" + module = load_module("pathlib.Path") + assert module == Path + + def test_load_module_non_string(self): + """Test that load_module returns the input if not a string.""" + + class DummyClass: + pass + + result = load_module(DummyClass) + assert result == DummyClass + + def test_get_config_dict_copy(self): + """Test creating an editable copy of a config dict.""" + config = config_dict.ConfigDict() + config.param1 = 42 + config.param2 = "test" + config.lock() + + # Original should be locked + with pytest.raises(AttributeError): + config.new_param = 100 + + # Copy should be unlocked + config_copy = get_config_dict_copy(config) + config_copy.new_param = 100 + + assert config_copy.new_param == 100 + assert not hasattr(config, "new_param") + + def test_instantiate_with_target(self): + """Test instantiating an object from config with _target_.""" + config = config_dict.ConfigDict() + config._target_ = "pathlib.Path" + # Path constructor takes positional argument, not "path" + # Let's use a different class for testing + config._target_ = "collections.namedtuple" + config.typename = "TestTuple" + config.field_names = ["field1", "field2"] + + result = instantiate(config) + # Should create a namedtuple class + assert hasattr(result, "_fields") + assert result._fields == ("field1", "field2") + + def test_instantiate_without_target(self): + """Test instantiate returns config when no _target_ is specified.""" + config = config_dict.ConfigDict() + config.param1 = 42 + config.param2 = "test" + + result = instantiate(config) + assert isinstance(result, config_dict.ConfigDict) + assert result.param1 == 42 + + def test_instantiate_with_partial(self): + """Test instantiate with _partial_ flag.""" + config = config_dict.ConfigDict() + config._target_ = "pathlib.Path" + config._partial_ = True + config.path = "/tmp/test" + + result = instantiate(config) + # Result should be a partial function + assert callable(result) + path_instance = result() + assert isinstance(path_instance, Path) + + def test_instantiate_skip(self): + """Test that _skip_instantiate_ prevents instantiation.""" + config = config_dict.ConfigDict() + config._target_ = "pathlib.Path" + config._skip_instantiate_ = True + config.path = "/tmp/test" + + result = instantiate(config) + assert isinstance(result, config_dict.ConfigDict) + assert result._target_ == "pathlib.Path" + + def test_instantiate_nested_config(self): + """Test instantiate with nested configurations.""" + inner_config = config_dict.ConfigDict() + inner_config.value = 42 + + outer_config = config_dict.ConfigDict() + outer_config.nested = inner_config + outer_config.simple = "test" + + result = instantiate(outer_config) + assert isinstance(result, 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