# BioMLWrapper Class

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

---

## Class Definition

```python
class BioMLWrapper:
    def __init__(self, neural_network: NeuralNetwork):
        """
        Initialize a new BioMLWrapper object.
        :param neural_network: A pre-initialized NeuralNetwork object
        """
        ...
```

---

## Attributes

| Attribute | Type | Description |
|-----------|------|-------------|
| `neural_network` | `NeuralNetwork` | 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, neural_network: NeuralNetwork)`**
  Initializes a new `BioMLWrapper` instance with a 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, lr_scheduler, optimizer, batch_size, loss_function)`**
  Trains the neural network.

- **`hyperparameter_tuning(self, param_grid, inputs, targets, epochs, loss_function)`**
  Performs hyperparameter tuning for the neural network.

- **`set_loss_function(self, loss_function)`**
  Sets the loss function 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.

- **`deploy_neural_network(self, host='0.0.0.0', port=5000)`**
  Deploys the neural network as a service.

---

### 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: 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 neural network
neural_network = NeuralNetwork()

# Create a BioMLWrapper instance
bio_ml_wrapper = BioMLWrapper(neural_network)

# 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_scheduler=None, optimizer="adam", batch_size=2, 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")
```

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