# ===================================================================
# Diffusion Model Configuration for Multi-System Pooled Embeddings
# ===================================================================

# --- Execution Target ---
# Specify which system's embeddings to train the diffusion model on.
# This corresponds to the group name in the HDF5 file (e.g., 0 for 'system_0').
system_to_train: 0

# --- Data Paths ---
# Path to the HDF5 file containing the pooled embeddings from the multi-system ChebNet script.
h5_file_path: "latent_reps/pooled_embeddings.h5"
# Template for the group name within the HDF5 file. The script will replace '{}' with 'system_to_train'.
dataset_group_key_template: "system_{}"
# Name of the dataset within each group.
dataset_name_in_group: "embeddings"
# Directory to save generated embeddings and checkpoints.
output_dir: "diffusion_output"


# --- Run Mode & Grid Search ---
# 'user_defined': Runs a single experiment with the parameters in the 'parameters' section.
# 'grid_search': Runs a series of experiments defined in 'grid_search_space'.
run_mode: "grid_search"

# --- Parameters for a single run (if run_mode is 'user_defined') ---
parameters:
  num_epochs: 50
  batch_size: 64
  learning_rate: 1e-5
  num_gen: 5000
  save_interval: 1000
  model_type: "mlp_v2"
  hidden_dim: 1024
  scheduler: "linear"
  diffusion_steps: 1400
  beta_start: 5e-6
  beta_end: 0.03
  num_instances: 6 # For partitioning grid search experiments

# --- Hyperparameter space for grid search (if run_mode is 'grid_search') ---
grid_search_space:
  # These parameters will be fixed across all grid search experiments
  # To vary them, you would need to add them to the 'curated_experiments' in the script.
  learning_rate: 1e-5
  num_epochs: 50
  model_type: "mlp_v2"
  hidden_dim: 1024

  # The script will generate a curated grid based on these values.
  # This section is for reference; the actual grid is built in the python script.
  example_beta_starts: [5e-6, 0.005]
  example_beta_ends: [0.03, 0.1]
  example_diffusion_steps: [500, 1400]