nn doesnt work on TOYv2.

works on TOYv1 but v2 is too small, early stopping set is empty. we might catch this/fail gracefully or allow empty ES sets (fall back to no ES if ES is too small) it's an edge case bug for tiny datasets so maybe a wontfix ```python from drevalpy.experiment import drug_response_experiment from drevalpy.models import MODEL_FACTORY from drevalpy.datasets import AVAILABLE_DATASETS naive_mean = MODEL_FACTORY["NaivePredictor"] # a naive model that just predicts the training mean enet = MODEL_FACTORY["ElasticNet"] # An Elastic Net based on drug fingerprints and gene expression of 1000 landmark genes simple_nn = MODEL_FACTORY["SimpleNeuralNetwork"] # A neural network based on drug fingerprints and gene expression of 1000 landmark genes toyv2 = AVAILABLE_DATASETS["TOYv2"](path_data="data") drug_response_experiment( models=[enet, simple_nn], baselines=[naive_mean], # If they are queried, ablation studies and robustness tests are not done for baselines. response_data=toyv2, n_cv_splits=2, # the number of cross validation splits. Should be higher in practice :) test_mode="LCO", # LCO means Leave-Cell-Line out. This means that the test and validation splits only contain unseed cell lines. run_id="my_first_run", path_data="data", # where the downloaded drug response and feature data is stored path_out="results", # results are stored here :) hyperparameter_tuning=False) # if True (default), hyperparameters of the models and baselines are tuned. --------------------------------------------------------------------------- RuntimeError Traceback (most recent call last) [/tmp/ipython-input-4-1050526458.py](https://localhost:8080/#) in <cell line: 0>() 8 toyv2 = AVAILABLE_DATASETS["TOYv2"](path_data="data") 9 ---> 10 drug_response_experiment( 11 models=[enet, simple_nn], 12 baselines=[naive_mean], # If they are queried, ablation studies and robustness tests are not done for baselines. 14 frames [/usr/local/lib/python3.11/dist-packages/pytorch_lightning/callbacks/early_stopping.py](https://localhost:8080/#) in _validate_condition_metric(self, logs) 151 if monitor_val is None: 152 if self.strict: --> 153 raise RuntimeError(error_msg) 154 if self.verbose > 0: 155 rank_zero_warn(error_msg, category=RuntimeWarning) RuntimeError: Early stopping conditioned on metric `val_loss` which is not available. Pass in or modify your `EarlyStopping` callback to use any of the following: `train_loss`, `train_loss_step`, `train_loss_epoch` ```