add docstrings for FineTuner class

flexynesis/main.py

@@ -324,6 +324,33 @@ def load_and_convert_config(self, config_path):
 import random, copy, logging
 
 class FineTuner(pl.LightningModule):
+    """
+    FineTuner class is designed for fine-tuning trained flexynesis models with flexible control over parameters such as 
+    learning rates and component freezing, utilizing cross-validation to optimize generalization.
+
+    This class allows the application of different configuration strategies to either freeze or unfreeze specific 
+    model components, while also exploring different learning rates to find the optimal setting. 
+    It carries out cross-validation to find the best combination of parameter freezing strategies and learning rates.
+
+    Attributes:
+        model (pl.LightningModule): The model instance to be fine-tuned.
+        dataset (Dataset): The dataset used for training and validation.
+        n_splits (int): Number of cross-validation splits.
+        batch_size (int): Batch size for training and validation.
+        learning_rates (list): List of learning rates to try during fine-tuning.
+        max_epoch (int): Maximum number of epochs for training.
+        freeze_configs (list of dicts): Configurations specifying which components of the model to freeze.
+
+    Methods:
+        apply_freeze_config(config): Apply a freezing configuration to the model components.
+        train_dataloader(): Returns a DataLoader for the training data of the current fold.
+        val_dataloader(): Returns a DataLoader for the validation data of the current fold.
+        training_step(batch, batch_idx): Executes a training step using the model's internal training logic.
+        validation_step(batch, batch_idx): Executes a validation step using the model's internal validation logic.
+        configure_optimizers(): Sets up the optimizer with the current learning rate and filtered trainable parameters.
+        run_experiments(): Executes the finetuning process across all configurations and learning rates, evaluates
+                           using cross-validation, and selects the best configuration based on validation loss.
+    """
     def __init__(self, model, dataset, n_splits=5, batch_size=32, learning_rates=None, max_epoch = 50, freeze_configs = None):
         super().__init__()
         logging.getLogger("lightning.pytorch").setLevel(logging.ERROR)