# BioMLWrapper Class
## Overview
The `BioMLWrapper` class integrates biological simulations (proteins, cells) with machine learning (PyTorch neural networks) to predict protein structures, simulate interactions, and analyze biological data. It provides methods for training, hyperparameter tuning, data export/import, model conversion between neural networks and DNA sequences, and visualization.

---

## Class Definition
```python
class BioMlWrapper:
    def __init__(self, input_size: int, output_size: int = 1, hidden_sizes: List[int] = None):
        """
        Initialize a new BioMlWrapper object.
        :param input_size: Size of the input layer
        :param output_size: Size of the output layer (default: 1)
        :param hidden_sizes: List of hidden layer sizes (default: None)
        """
        ...
```

---

## Attributes


| Attribute | Type | Description |
|-----------|------|-------------|
| `neural_network` | `PyTorchNeuralNetwork` | PyTorch neural network used for predictions and training. |
| `proteins` | `List[Protein]` | List of proteins in the simulation. |
| `cells` | `List[Cell]` | List of cells in the simulation. |
| `logger` | `logging.Logger` | Logger for tracking simulation events. |
| `dna_encoding` | `Dict[str, str]` | Mapping of nucleotides to binary. |
| `dna_decoding` | `Dict[str, str]` | Mapping of binary to nucleotides. |

---

## Methods

### Initialization and Management
- **`__init__(self, input_size: int, output_size: int = 1, hidden_sizes: List[int] = None)`**
  Initializes a new `BioMlWrapper` instance with a PyTorch neural network.
- **`add_protein(self, protein: Protein)`**
  Adds a protein to the simulation.
- **`add_cell(self, cell: Cell)`**
  Adds a cell to the simulation.

---

### Simulation
- **`simulate_protein_interactions(self)`**
  Simulates interactions for all proteins.
- **`simulate_cell_behavior(self)`**
  Simulates behavior for all cells.
- **`visualize_protein_interactions(self, protein: Protein)`**
  Visualizes interactions for a specific protein.
- **`visualize_cell_behavior(self, cell: Cell)`**
  Visualizes behavior for a specific cell.
- **`describe_simulation(self)`**
  Describes the current state of the simulation.

---

### Prediction and Training
- **`predict_protein_structure(self, protein: Protein)`**
  Predicts the structure of a protein using the neural network.
- **`predict_protein_structures_batch(self, proteins: list)`**
  Predicts structures for a batch of proteins.
- **`train_neural_network(self, inputs, targets, epochs=100, lr=0.01, loss_function='mse')`**
  Trains the neural network.
- **`hyperparameter_tuning(self, param_grid, inputs, targets, epochs, loss_function)`**
  Performs hyperparameter tuning for the neural network.

---

### Data Export and Import
- **`export_simulation_data(self, file_path: str)`**
  Exports simulation data to a CSV file.
- **`import_simulation_data(self, file_path: str)`**
  Imports simulation data from a CSV file.
- **`save_simulation_state(self, file_path: str)`**
  Saves the current simulation state to a file.
- **`load_simulation_state(self, file_path: str)`**
  Loads a simulation state from a file.

---

### Neural Network Management
- **`save_neural_network(self, file_path: str)`**
  Saves the neural network to a file.
- **`load_neural_network(self, file_path: str)`**
  Loads the neural network from a file.
- **`plot_loss(self, losses, title="Training Loss", xlabel="Epoch", ylabel="Loss")`**
  Plots the training loss.
- **`get_layer_outputs(self)`**
  Retrieves outputs from each layer of the neural network.
- **`get_gradients(self)`**
  Retrieves gradients from the neural network.
- **`get_parameter_history(self)`**
  Retrieves the history of parameter changes.
- **`analyze_gradients(self)`**
  Analyzes gradients in the neural network.
- **`plot_parameter_changes(self)`**
  Plots changes in neural network parameters.

---

### Architecture Suggestions
- **`suggest_architecture(self, input_size, output_size, task_type='classification', data_type='tabular', depth=3, temperature=1.0)`**
  Suggests a neural network architecture.
- **`apply_suggested_architecture(self, input_size, output_size, task_type='classification', data_type='tabular', depth=3)`**
  Applies the suggested architecture to the neural network.
- **`train_with_suggested_architecture(self, inputs, targets, input_size, output_size, optimizer=None, task_type='classification', data_type='tabular', depth=3, epochs=100, lr=0.01, batch_size=32)`**
  Trains the neural network with the suggested architecture.
- **`rank_network_performance(self, test_inputs, test_targets, temperature, task_type='classification', creativity_threshold=0.5)`**
  Ranks the performance of the neural network.

---

### Model Conversion
- **`convert_model_to_dna(self, file_path: str, output_file: str)`**
  Converts a trained model to DNA sequences.
- **`convert_dna_to_model(self, dna_file: str)`**
  Converts DNA sequences back to model parameters.

---

### Utility Methods
- **`_prepare_input_data(self, protein: Protein)`**
  Prepares input data for the neural network.
- **`_interpret_output(self, output_tensor: torch.Tensor)`**
  Interprets the output from the neural network.
- **`_float_to_binary(self, f)`**
  Converts a float to its binary representation.
- **`_binary_to_dna(self, binary)`**
  Converts a binary string to a DNA sequence.
- **`_dna_to_parameter(self, sequence)`**
  Converts a DNA sequence to a parameter value.
- **`_dna_to_ndarray(self, dna, layer)`**
  Converts a DNA sequence to a NumPy array.
- **`_dna_to_binary(self, dna)`**
  Converts a DNA sequence to its binary representation.
- **`_binary_to_float(self, binary)`**
  Converts a binary string to a float.

---

## Example Usage
```python
# Initialize a BioMlWrapper instance
bio_ml_wrapper = BioMlWrapper(input_size=20, output_size=1, hidden_sizes=[64, 32])

# Add proteins and cells
protein = Protein(name="Protein1", sequence="ACDEFGHIKLMNPQRSTVWY")
cell = Cell(name="Cell1", cell_type="epithelial")
bio_ml_wrapper.add_protein(protein)
bio_ml_wrapper.add_cell(cell)

# Simulate interactions and behavior
bio_ml_wrapper.simulate_protein_interactions()
bio_ml_wrapper.simulate_cell_behavior()

# Predict protein structure
predicted_structure = bio_ml_wrapper.predict_protein_structure(protein)

# Train the neural network
inputs = [[0.1, 0.2], [0.3, 0.4]]
targets = [[0.5], [0.6]]
losses = bio_ml_wrapper.train_neural_network(inputs, targets, epochs=10, lr=0.01, loss_function="mse")

# Export and import simulation data
bio_ml_wrapper.export_simulation_data("simulation_data.csv")
bio_ml_wrapper.import_simulation_data("simulation_data.csv")

# Save and load the neural network
bio_ml_wrapper.save_neural_network("neural_network.pkl")
bio_ml_wrapper.load_neural_network("neural_network.pkl")

# Convert model to DNA and back
bio_ml_wrapper.convert_model_to_dna("model.pkl", "dna_sequences.txt")
bio_ml_wrapper.convert_dna_to_model("dna_sequences.txt")
```

---

## Notes
- The `BioMlWrapper` class depends on the `Protein` and `Cell` classes for biological simulations.
- The `PyTorchNeuralNetwork` class is used for all neural network operations.
- The `convert_model_to_dna` and `convert_dna_to_model` methods enable novel model storage and retrieval using DNA sequences.
