replicated mecc

.gitignore

@@ -0,0 +1,7 @@
+.venv
+.env
+__pycaches__
+datasets/
+temp.*
+test.*
+

.vscode/launch.json

@@ -0,0 +1,15 @@
+{
+    // Use IntelliSense to learn about possible attributes.
+    // Hover to view descriptions of existing attributes.
+    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Python Debugger: Current File",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "${file}",
+            "console": "integratedTerminal"
+        }
+    ]
+}

generate_data_consensus.py

@@ -0,0 +1,206 @@
+import os
+import json
+import time
+import jax
+import jax.numpy as jnp
+import numpy as np
+import networkx as nx
+import networkx.algorithms.community as nx_comm
+from tqdm import tqdm
+import sims
+
+
+
+def generate_connected_graph(rng: np.random.Generator, num_agents: int, graph_type: str) -> tuple[nx.Graph, str, np.ndarray]:
+    """
+    Generates a random, undirected, unweighted, connected graph.
+    It randomly selects a NetworkX algorithm and retries until the graph is connected.
+
+    Returns:
+        A tuple containing the networkx Graph object, the algorithm name, and the adjacency matrix.
+    """
+
+    G = None
+    
+
+    if graph_type == "erdos_renyi":
+    
+        # p = 2.0 * np.log(num_agents) / num_agents
+        p = 0.5
+        G = nx.erdos_renyi_graph(num_agents, p, seed=rng)
+    
+    elif graph_type == "watts_strogatz":
+        k = 2
+        p = 0.1
+        G = nx.watts_strogatz_graph(num_agents, k, p, seed=rng)
+    
+    elif graph_type == "barabasi_albert":
+        m = 2
+        if m >= num_agents: m = max(1, num_agents - 1)
+        G = nx.barabasi_albert_graph(num_agents, m, seed=rng)
+    
+    elif  graph_type == "powerlaw_cluster":
+        m = 3 # Number of random edges to add for each new node
+        p = 0.1 # Probability of adding a triangle after adding a random edge
+        if m >= num_agents: m = max(1, num_agents - 1)
+        G = nx.powerlaw_cluster_graph(num_agents, m, p, seed=rng)
+
+    # Add self-loops, as they are often assumed in consensus algorithms
+    G.add_edges_from([(i, i) for i in range(num_agents)])
+    adj_matrix = nx.to_numpy_array(G, dtype=np.float32)
+
+    return G, graph_type, adj_matrix
+
+def calculate_graph_metrics(G: nx.Graph) -> dict:
+    """
+    Calculates and returns a dictionary of key graph metrics.
+
+    This function computes basic properties, connectivity, clustering,
+    community structure, and spectral properties of the graph.
+    Computationally expensive metrics are skipped for larger graphs to ensure performance.
+    """
+    metrics = {}
+    num_nodes = G.number_of_nodes()
+    num_edges = G.number_of_edges()
+
+    # --- Basic Properties ---
+    metrics["number_of_nodes"] = num_nodes
+    metrics["number_of_edges"] = num_edges
+    metrics["average_degree"] = (2 * num_edges / num_nodes) if num_nodes > 0 else 0
+    metrics["edge_density"] = nx.density(G)
+
+    # --- Connectivity ---
+    metrics["is_connected"] = nx.is_connected(G)
+    metrics["number_connected_components"] = nx.number_connected_components(G)
+
+    # --- Clustering ---
+    metrics["average_clustering_coefficient"] = nx.average_clustering(G)
+    metrics["clustering_coefficient"] = nx.clustering(G) # Per-node clustering
+
+    # --- Distance-Based Metrics (for connected graphs) ---
+    # These are computationally intensive and only run on smaller, connected graphs.
+    if metrics["is_connected"] and num_nodes < 250:
+        metrics["average_shortest_path_length"] = nx.average_shortest_path_length(G)
+        metrics["diameter"] = nx.diameter(G)
+        metrics["eccentricity"] = nx.eccentricity(G)
+    else:
+        # Set to None if graph is disconnected or too large
+        metrics["average_shortest_path_length"] = None
+        metrics["diameter"] = None
+        metrics["eccentricity"] = None
+
+    # --- Spectral & Community Metrics (Potentially Slow) ---
+    # These are also limited to smaller graphs.
+    if 1 < num_nodes < 500:
+        # Eigenvalues of the Laplacian matrix
+        try:
+            laplacian_eigenvalues = sorted(nx.laplacian_spectrum(G))
+            metrics["laplacian_eigenvalues"] = laplacian_eigenvalues
+            # The second-smallest eigenvalue of the Laplacian matrix
+            metrics["algebraic_connectivity"] = laplacian_eigenvalues[1]
+        except Exception:
+            metrics["laplacian_eigenvalues"] = None
+            metrics["algebraic_connectivity"] = None
+
+        # Modularity using the Louvain community detection algorithm
+        if num_edges > 0:
+            try:
+                communities = nx_comm.louvain_communities(G, seed=123)
+                metrics["modularity"] = nx_comm.modularity(G, communities)
+            except Exception:
+                metrics["modularity"] = None # Algorithm may fail on some graphs
+        else:
+            metrics["modularity"] = None
+    else:
+        metrics["laplacian_eigenvalues"] = None
+        metrics["algebraic_connectivity"] = None
+        metrics["modularity"] = None
+        
+    return metrics
+
+def generate_multiple_initial_states(key: jax.Array, num_sims: int, num_agents: int, max_range: float) -> jax.Array:
+    """Generate a batch of unique random initial states for the agents."""
+    return jax.random.uniform(
+        key,
+        shape=(num_sims, num_agents),
+        minval=-max_range,
+        maxval=max_range
+    )
+
+def main():
+    """Main script to generate and save the consensus simulation dataset."""
+    # --- Configuration ---
+    GRAPH_GEN_ALGOS = ["erdos_renyi", "barabasi_albert", "powerlaw_cluster", "watts_strogatz"]
+    AGENT_COUNTS = range(5, 51, 5)# 5, 10, ..., 50 agents
+    GRAPHS_PER_AGENT_COUNT = 100
+    GRAPHS_PER_GRAPH_ALGO = GRAPHS_PER_AGENT_COUNT // len(GRAPH_GEN_ALGOS)
+    SIMS_PER_GRAPH = 100
+    OUTPUT_DIR = "datasets/consensus_dataset"
+    
+    # --- Setup ---
+    seed = int(time.time())
+    main_jax_key = jax.random.PRNGKey(seed)
+    numpy_rng = np.random.default_rng(seed=seed + 1)
+
+    print(f"🚀 Starting data generation...")
+    print(f"Saving dataset to directory: '{OUTPUT_DIR}/'")
+    os.makedirs(OUTPUT_DIR, exist_ok=True)
+    
+    # --- Main Generation Loop ---
+    for n_agents in tqdm(AGENT_COUNTS, desc="Overall Progress"):
+        agent_folder = os.path.join(OUTPUT_DIR, f"agents_{n_agents}")
+        os.makedirs(agent_folder, exist_ok=True)
+        
+        for graph_idx in tqdm(range(GRAPHS_PER_AGENT_COUNT), desc=f"Graphs for N={n_agents}", leave=False):
+            graph_algo_idx = graph_idx // GRAPHS_PER_GRAPH_ALGO 
+            
+            # 1. Configure the simulation using the imported class
+            config = sims.consensus.ConsensusConfig()
+
+            config.num_agents=n_agents
+            config.num_sims=SIMS_PER_GRAPH
+
+            # 2. Generate graph and its metrics
+            G, graph_type, adj_matrix_np = generate_connected_graph(numpy_rng, config.num_agents, GRAPH_GEN_ALGOS[graph_algo_idx])
+            adj_matrix_jax = jnp.array(adj_matrix_np)
+            graph_metrics = calculate_graph_metrics(G)
+            graph_metrics["graph_type"] = graph_type
+
+            # 3. Generate initial states for the 100 simulations
+            main_jax_key, states_key = jax.random.split(main_jax_key)
+            initial_states = generate_multiple_initial_states(
+                states_key, config.num_sims, config.num_agents, config.max_range
+            )
+
+            # 4. Run all simulations using the imported JAX function
+            trajectories = sims.consensus.run_consensus_sim(adj_matrix_jax, initial_states, config)
+
+
+            
+            # 5. Package all data into a dictionary for logging
+            log_data = {
+                "simulation_config": {
+                    "num_agents": config.num_agents,
+                    "num_sims": config.num_sims,
+                    "num_time_steps": config.num_time_steps,
+                    "step_size": config.step_size,
+                    "max_range": config.max_range,
+                    "directed": config.directed,
+                    "weighted": config.weighted,
+                
+                },
+                "graph_metrics": graph_metrics,
+                "adjacency_matrix": adj_matrix_np.tolist(),
+                "initial_states": initial_states.tolist(),
+                "trajectories": trajectories.tolist()
+            }
+            
+            # 6. Save the complete log to a JSON file
+            file_path = os.path.join(agent_folder, f"graph_{graph_idx:02d}.json")
+            with open(file_path, 'w') as f:
+                json.dump(log_data, f, indent=2)
+                
+    print(f"\n✅ Data generation complete! Check the '{OUTPUT_DIR}' folder.")
+
+if __name__ == "__main__":
+    main()

generate_data_kuramoto.py

@@ -0,0 +1,162 @@
+import os
+import json
+import time
+import jax
+import jax.numpy as jnp
+import numpy as np
+import networkx as nx
+from tqdm import tqdm
+from functools import partial
+from dataclasses import asdict
+import sims
+# --- Import necessary components from your Kuramoto simulation file ---
+from sims.kuramoto import (
+    KuramotoConfig,
+    run_kuramoto_simulation,
+    phase_coherence,
+    mean_frequency,
+)
+
+from generate_data_consensus import generate_connected_graph, calculate_graph_metrics
+
+# ==============================================================================
+# SECTION 1: BATCHED OPERATIONS FOR EFFICIENT DATA GENERATION
+# ==============================================================================
+
+@partial(jax.jit, static_argnames=("config",))
+def run_batched_simulations(
+    thetas0_batch: jax.Array,    # (num_sims, N)
+    omegas_batch: jax.Array,     # (num_sims, N)
+    adj_mat: jax.Array,          # (N, N)
+    config: KuramotoConfig
+) -> tuple[jax.Array, jax.Array, jax.Array]:
+    """
+    Runs many Kuramoto simulations in parallel for the same graph but different
+    initial conditions, and performs analysis.
+
+    Returns:
+        A tuple containing:
+        - All trajectories (num_sims, T, N)
+        - Final coherence R(T) for each sim (num_sims,)
+        - Mean frequencies for each sim (num_sims, N)
+    """
+    # Create a batched version of the simulation runner using vmap.
+    # This maps the function over the first axis of thetas0_batch and omegas_batch.
+    vmapped_runner = jax.vmap(
+        run_kuramoto_simulation,
+        in_axes=(0, 0, None, None), # Map over thetas0, omegas; adj_mat and config are fixed
+        out_axes=0
+    )
+    all_trajectories = vmapped_runner(thetas0_batch, omegas_batch, adj_mat, config)
+    
+    # Analyze the results in a batched manner
+    # phase_coherence naturally works on the time axis, so we vmap over the sim axis.
+    vmapped_coherence = jax.vmap(phase_coherence)(all_trajectories) # -> (num_sims, T)
+    final_coherence = vmapped_coherence[:, -1] # -> (num_sims,)
+
+    # vmap the mean_frequency calculation over trajectories and omegas
+    vmapped_mean_freq = jax.vmap(mean_frequency, in_axes=(0, 0, None, None))
+    all_mean_freqs = vmapped_mean_freq(all_trajectories, omegas_batch, adj_mat, config)
+
+    return all_trajectories, final_coherence, all_mean_freqs
+
+def generate_batched_initial_states(key: jax.Array, num_sims: int, config: KuramotoConfig):
+    """Generates a batch of initial states."""
+    keys = jax.random.split(key, num_sims)
+    
+    # We will just write a new generator that is vmap-friendly
+    @jax.vmap
+    def generate_single_state(k):
+        key_theta, key_omega = jax.random.split(k)
+        thetas0 = jax.random.uniform(key_theta, (config.num_agents,), minval=0, maxval=2 * jnp.pi)
+        omegas = jax.random.normal(key_omega, (config.num_agents,)) # Using std=1, mean=0
+        return thetas0, omegas
+        
+    thetas0_batch, omegas_batch = generate_single_state(keys)
+    return thetas0_batch, omegas_batch
+
+
+def main():
+    """Main script to generate and save the Kuramoto simulation dataset."""
+    # --- Configuration ---
+    GRAPH_GEN_ALGOS = ["erdos_renyi", "barabasi_albert", "powerlaw_cluster", "watts_strogatz"]
+    AGENT_COUNTS = range(5, 51, 5)  # 5, 10, ..., 50 agents
+    # AGENT_COUNTS = [50]
+    GRAPHS_PER_AGENT_COUNT = 100
+    GRAPHS_PER_GRAPH_ALGO = GRAPHS_PER_AGENT_COUNT // len(GRAPH_GEN_ALGOS)
+    SIMS_PER_GRAPH = 100
+    OUTPUT_DIR = "datasets/kuramoto_dataset"
+    
+    # --- Setup ---
+    seed = int(time.time())
+    main_key = jax.random.PRNGKey(seed)
+    numpy_rng = np.random.default_rng(seed=seed + 1)
+
+    print(f"🚀 Starting Kuramoto data generation...")
+    print(f"Saving dataset to: '{OUTPUT_DIR}/'")
+    os.makedirs(OUTPUT_DIR, exist_ok=True)
+    
+    # --- Main Loop ---
+    for n_agents in tqdm(AGENT_COUNTS, desc="Agent Counts"):
+        agent_folder = os.path.join(OUTPUT_DIR, f"agents_{n_agents}")
+        os.makedirs(agent_folder, exist_ok=True)
+        
+        for graph_idx in tqdm(range(GRAPHS_PER_AGENT_COUNT), desc=f"Graphs for N={n_agents}", leave=False):
+            
+            graph_algo_idx = graph_idx // GRAPHS_PER_GRAPH_ALGO 
+            # 1. Setup config, keys, and graph
+            config = KuramotoConfig()
+            config.num_agents=n_agents
+            config.coupling=1.5 
+            config.T=15.0
+            main_key, graph_key, state_key = jax.random.split(main_key, 3)
+            
+            G, graph_type, adj_matrix_np = generate_connected_graph(numpy_rng, config.num_agents, GRAPH_GEN_ALGOS[graph_algo_idx])
+            adj_matrix_jax = jnp.array(adj_matrix_np)
+            graph_metrics = calculate_graph_metrics(G)
+            graph_metrics["graph_type"] = graph_type
+
+            # 2. Generate a batch of initial conditions
+            thetas0_batch, omegas_batch = generate_batched_initial_states(state_key, SIMS_PER_GRAPH, config)
+
+            # 3. Run all simulations and analyses in a single, efficient call
+            trajectories, final_R, mean_freqs = run_batched_simulations(
+                thetas0_batch, omegas_batch, adj_matrix_jax, config
+            )
+            trajectories.block_until_ready() # Ensure computation is finished
+
+            # 4. Package all data for logging
+            log_data = {
+                "config": {
+                    "num_agents" : config.num_agents,
+                    "coupling" : config.coupling,
+                    "dt" : config.dt,
+                    "T" : config.T,
+                    "normalize_by_degree" : config.normalize_by_degree,
+                    "directed" : config.directed,
+                    "weighted" : config.weighted,
+                },
+                
+                "graph_metrics": graph_metrics,
+                "adjacency_matrix": adj_matrix_np.tolist(),
+                "initial_conditions": {
+                    "thetas0": thetas0_batch.tolist(),
+                    "omegas": omegas_batch.tolist()
+                },
+                "results": {
+                    "final_coherence": final_R.tolist(),
+                    "mean_frequencies": mean_freqs.tolist()
+                },
+                # Optionally save full trajectories. Warning: can create large files.
+                "trajectories": trajectories.tolist() 
+            }
+            
+            # 6. Save the complete log to a JSON file
+            file_path = os.path.join(agent_folder, f"graph_{graph_idx:02d}.json")
+            with open(file_path, 'w') as f:
+                json.dump(log_data, f, indent=2)
+                
+    print(f"\n✅ Data generation complete! Check the '{OUTPUT_DIR}' folder.")
+
+if __name__ == "__main__":
+    main()

model.py

@@ -0,0 +1,136 @@
+import jax
+from jax import random
+import jax.numpy as jnp
+from train import ModelConfig, TrainConfig
+import optax
+from functools import partial
+
+def init_linear_layer(
+        key: jax.Array,
+        in_features: int, 
+        out_features: int,
+        use_bias: bool = True
+        ):
+    
+    """
+    Initializes the weights and biases in a linear layer.
+    """
+    key_w, key_b = random.split(key)
+    limit = jnp.sqrt(6/in_features)
+    W = random.uniform(key_w, (in_features, out_features), minval=-limit, maxval=limit)
+    params = {'W': W}
+    if use_bias:
+        b = random.uniform(key_b, (out_features,), minval=-limit, maxval=limit)
+        params['b'] = b
+    return params
+
+def init_fn(key: jax.Array, config: ModelConfig):
+    """
+    Initializes all model parameters. Returns a pytree
+    """
+
+    key_embed, key_translate, key_attn_proj, key_head = random.split(key, 4)
+
+    params = {
+        "agent_embeddings" : {
+            "weight" : random.normal(key_embed, shape=(config.num_agents, config.embedding_dim))
+        },
+        "translate": init_linear_layer(key_translate, config.input_dim, config.embedding_dim),
+        "attn_proj": init_linear_layer(key_attn_proj, config.embedding_dim, 2 * config.embedding_dim, use_bias=False),
+        "head": init_linear_layer(key_head, config.embedding_dim, config.output_dim)
+    }
+
+    return params
+
+def forward(params: dict, input_timesteps: jax.Array, config: ModelConfig):
+    """
+    Model's forward function. Takes in the parameters and inptu timesteps, returns predictions
+    """
+    batch_size, num_agents, _ = input_timesteps.shape
+
+    agent_embed = params["agent_embeddings"]["weight"]
+    agent_embed = jnp.broadcast_to(agent_embed, (batch_size, num_agents, config.embedding_dim))
+
+    attn_proj_out = agent_embed @ params["attn_proj"]['W']
+    k, q = jnp.split(attn_proj_out, 2, axis=-1)
+    v = input_timesteps @ params["translate"]['W'] + params["translate"]['b']
+    att_scores = (q @ k.transpose(0, 2, 1) )/ jnp.sqrt(num_agents)
+    att_weights = jax.nn.softmax(att_scores, axis=-1)
+    weighted_average = att_weights @ v
+    prediction = weighted_average @ params["head"]['W'] + params["head"]['b']
+
+    return prediction
+
+def get_attention_fn(params: dict, config: ModelConfig):
+    """
+    Calculates and returns the learned attention matrix between agents.
+    This is a pure function for analysis.
+    """
+    embeddings = params['agent_embeddings']['weight']
+    
+    # Project embeddings to get keys (k) and queries (q) for the global graph
+    attn_proj_out = embeddings @ params['attn_proj']['W']
+    k, q = jnp.split(attn_proj_out, 2, axis=-1)
+
+    # Note: Using sqrt(embedding_dim) as in the original get_attention method
+    attn_scores = (q @ k.T) / jnp.sqrt(q.shape[-1])
+    
+    return jnp.asarray(attn_scores) # Return as NumPy array for logging
+
+def train_model(config: ModelConfig, inputs: jax.Array, targets: jax.Array,
+                true_graph: jax.Array,
+                train_config: TrainConfig,
+                ):
+    
+    key = random.PRNGKey(0)
+    key, init_key = random.split(key)
+    params = init_fn(init_key, config)
+
+    optimizer = optax.adamw(train_config.learning_rate)
+    opt_state = optimizer.init(params)
+
+    def loss_fn(p, x_batch, y_batch, config):
+        predictions = forward(p, x_batch, config)
+        loss = jnp.mean(jnp.abs(predictions - y_batch))
+        return loss
+
+    @partial(jax.jit, static_argnames=['config'])
+    def update_step(params, opt_state, x_batch, y_batch, config):
+        # FIX 1: Pass all necessary arguments to the loss function here.
+        loss_val, grads = jax.value_and_grad(loss_fn)(params, x_batch, y_batch, config)
+
+        updates, new_opt_state = optimizer.update(grads, opt_state, params)
+        new_params = optax.apply_updates(params, updates)
+
+        return new_params, new_opt_state, loss_val
+
+    loss_history = {f"epoch_{i}": [] for i in range(train_config.epochs)}
+    # FIX 2: Initialize with empty lists `[]` instead of `None`.
+    graphs = {f"epoch_{i}": [] for i in range(train_config.epochs)}
+    num_batches = len(inputs)
+
+    for epoch in range(train_config.epochs):
+        running_loss = 0.0
+        for batch_num in range(num_batches):
+            x, y = inputs[batch_num], targets[batch_num]
+            # FIX 3: Pass the `config` object, as it's a static argument for JIT.
+            params, opt_state, loss_val = update_step(params, opt_state, x, y, config)
+            running_loss += loss_val
+
+        epoch_loss = running_loss / num_batches
+        loss_history[f"epoch_{epoch}"].append(epoch_loss)
+
+        if train_config.verbose and (epoch + 1) % 10 == 0:
+            print(f"Epoch {epoch+1:3d} | Loss: {epoch_loss:.6f}")
+
+        if train_config.log and epoch % train_config.log_epoch_interval == 0:
+            attn = get_attention_fn(params, config)
+            graphs[f"epoch_{epoch}"].append(attn)
+    
+    all_logs = {
+        "loss_history": loss_history,
+        "graphs": graphs,
+        "true_graph": true_graph,
+    }
+
+    return params, all_logs

pyproject.toml

@@ -10,6 +10,9 @@ dependencies = [
     "jax[cuda12]>=0.7.0",
     "jupyter>=1.1.1",
     "matplotlib>=3.10.3",
+    "networkx>=3.5",
+    "optax>=0.2.5",
+    "scikit-learn>=1.7.1",
     "seaborn>=0.13.2",
     "tqdm>=4.67.1",
-]
+]

sims/__init__.py

@@ -0,0 +1,2 @@
+from .consensus import *
+from .kuramoto import *

sims/consensus.py

@@ -10,7 +10,7 @@ class ConsensusConfig:
     Config class for Consensus dynamics sims
     """
     num_sims: int = 500 # Number of consensus sims
-    num_agents: int = 5# Number of agents in the consensus simulation
+    num_agents: int = 5 # Number of agents in the consensus simulation
     max_range: float = 1 # Max range of values each agent can take
     step_size: float = 0.1 # Target range for length of simulation
     directed: bool = False # Consensus graph directed?
@@ -61,6 +61,10 @@ def generate_random_adjacency_matrix(key: jax.Array, config: ConsensusConfig):
         config: ConsensusConfig
             Config for Consensus dyanmics
 
+    Returns
+    ---------------------
+    adj_matrices: jax.Array (num_agents, num_agents)
+        Random matrix 
     """    
     rand_matrix =  jax.random.uniform(key, shape=(config.num_agents, config.num_agents))
     # idxs = jnp.arange(config.num_agents)
@@ -90,7 +94,7 @@ def generate_random_agent_states(key: jax.Array, config: ConsensusConfig):
 
     Returns
     ---------------------
-        rand_states: jax.Array (num_agents, 1)
+        rand_states: jax.Array (num_sims, num_agents)
 
     """
     rand_states = jax.random.uniform(key, shape=(config.num_sims, config.num_agents), minval=-config.max_range, maxval=config.max_range)
@@ -113,7 +117,7 @@ def run_consensus_sim(adj_mat: jax.Array, initial_agent_state: jax.Array, config
         config: ConsensusConfig
             Config for Consensus dynamics
     """ 
-
+    # batched consensus step (meant for many initial states)
     batched_consensus_step = jax.vmap(consensus_step, in_axes=(None, 0, None), out_axes=0)
     
     def step(x_prev, _):

sims/kuramoto.py

@@ -5,26 +5,18 @@ from functools import partial
 import numpy as np
 import matplotlib.pyplot as plt
 
-# -------------------- Configuration --------------------
-@dataclass(frozen=True)
 class KuramotoConfig:
     """Configuration for the Kuramoto model simulation."""
     num_agents: int = 10         # N: Number of oscillators
     coupling: float = 1.0        # K: Coupling strength
     dt: float = 0.01             # Δt: Integration time step
     T: float = 10.0              # Total simulation time
-    
-    # Adjacency matrix properties
-    normalize_by_degree: bool = True
+    time_steps: int = int(T/dt)
+    normalize_by_degree: bool = False
     directed: bool = False
     weighted: bool = False
 
-    @property
-    def num_time_steps(self) -> int:
-        """Total number of simulation steps."""
-        return int(self.T / self.dt)
 
-# -------------------- Core Dynamics --------------------
 @partial(jax.jit, static_argnames=("config",))
 def kuramoto_derivative(theta: jax.Array,        # (N,) phase angles
                         omega: jax.Array,        # (N,) natural frequencies
@@ -88,7 +80,7 @@ def run_kuramoto_simulation(
         scan_fn,
         thetas0,
         None,
-        length=config.num_time_steps
+        length=config.time_steps
     )
     return trajectory
 

train.py

@@ -0,0 +1,19 @@
+from dataclasses import dataclass
+
+class ModelConfig:
+    num_agents: int = 10
+    embedding_dim: int = 64
+    input_dim: int = 1
+    output_dim: int = 1
+    simulation_type:str = "consensus"
+
+class TrainConfig:
+    epochs: float = 100
+    learning_rate: float = 1e-3
+    verbose: bool = True
+    log: bool = True
+    log_epoch_interval: int = 10
+    
+
+
+

train_and_eval.py

@@ -0,0 +1,259 @@
+import os
+import json
+import jax
+import jax.numpy as jnp
+import numpy as np
+from dataclasses import dataclass, field, asdict
+from enum import Enum
+from tqdm import tqdm
+from sklearn.metrics import f1_score
+import uuid
+import pickle
+import sys
+
+
+# Import from your existing model file
+from model import ModelConfig, TrainConfig, train_model, get_attention_fn
+
+# Define an Enum for the data source for clarity and type safety
+
+# Overwrite the original TrainConfig to include our new parameters
+
+class TrainConfig:
+    """Configuration for the training process."""
+    learning_rate: float = 1e-3
+    epochs: int = 100
+    batch_size: int = 4096
+    verbose: bool = False  # Set to True to see epoch loss during training
+    log: bool = True
+    log_epoch_interval: int = 10
+    
+    # --- New parameters for this script ---
+    data_directory:str = "datasets/" + sys.argv[1] + "_dataset"
+    # Threshold for converting attention scores to a binary graph
+    f1_threshold: float = -0.4
+    
+
+def prepare_data_for_model(trajectories: np.ndarray, batch_size: int) -> tuple[np.ndarray, np.ndarray]:
+    """
+    Converts simulation trajectories into input-output pairs for the model.
+    Input: state at time t. Target: state at time t+1.
+    
+    Args:
+        trajectories: A numpy array of shape (num_sims, num_timesteps, num_agents).
+        batch_size: The desired batch size for training.
+
+    Returns:
+        A tuple of (batched_inputs, batched_targets).
+    """
+    # For each simulation, create (input, target) pairs
+    all_inputs = []
+    all_targets = []
+    
+    num_sims, num_timesteps, num_agents = trajectories.shape
+    
+    # trajectories = np.reshape(trajectories, shape=(num_sims * num_timesteps, num_agents))
+
+    for i_sim in range(num_sims):
+        for j_tstep in range(num_timesteps-1):
+            input = trajectories[i_sim, j_tstep, :]
+            target = trajectories[i_sim, j_tstep + 1, :]
+            all_inputs.append(input)
+            all_targets.append(target)
+
+    all_indices = np.arange(len(all_inputs))
+    np.random.shuffle(all_indices)
+    all_inputs = np.array(all_inputs)
+    all_targets = np.array(all_targets)
+
+    all_inputs = all_inputs[all_indices]
+    all_targets = all_targets[all_indices]
+            
+    # for sim_idx in range(num_sims):
+    #     # Input is state from t=0 to t=T-2
+    #     inputs = trajectories[sim_idx, :-1, :]
+    #     # Target is state from t=1 to t=T-1
+    #     targets = trajectories[sim_idx, 1:, :]
+    #     all_inputs.append(inputs)
+    #     all_targets.append(targets)
+        
+    # Concatenate all pairs from all simulations
+    # Shape -> (num_sims * (num_timesteps - 1), num_agents)
+    # full_dataset_inputs = np.concatenate(all_inputs, axis=0)
+    # full_dataset_targets = np.concatenate(all_targets, axis=0)
+    
+    # Reshape to have a feature dimension
+    # Shape -> (total_samples, num_agents, 1)
+    full_dataset_inputs = np.expand_dims(all_inputs, axis=-1)
+    full_dataset_targets = np.expand_dims(all_targets, axis=-1)
+    
+    # Create batches
+    num_samples = full_dataset_inputs.shape[0]
+    num_batches = num_samples // batch_size
+    
+    # Truncate to full batches
+    truncated_inputs = full_dataset_inputs[:num_batches * batch_size]
+    truncated_targets = full_dataset_targets[:num_batches * batch_size]
+    
+    # Reshape into batches
+    # Shape -> (num_batches, batch_size, num_agents, 1)
+    batched_inputs = truncated_inputs.reshape(num_batches, batch_size, num_agents, 1)
+    batched_targets = truncated_targets.reshape(num_batches, batch_size, num_agents, 1)
+    
+    return batched_inputs, batched_targets
+
+def calculate_f1_score(
+    params: dict, 
+    model_config: ModelConfig, 
+    true_graph: np.ndarray, 
+    threshold: float
+) -> float:
+    """
+    Extracts the learned attention graph, thresholds it, and computes the F1 score.
+    """
+    # Get the learned attention matrix (N, N)
+    learned_graph_scores = np.array(get_attention_fn(params, model_config))
+    
+    # Normalize scores to [0, 1] for consistent thresholding (optional but good practice)
+    # This uses min-max scaling on the flattened array
+    # flat_scores = learned_graph_scores.flatten()
+    # min_s, max_s = flat_scores.min(), flat_scores.max()
+    # if max_s > min_s:
+    #     learned_graph_scores = (learned_graph_scores - min_s) / (max_s - min_s)
+
+    # Threshold to get a binary predicted graph
+    predicted_graph = (learned_graph_scores > threshold).astype(int)
+    
+    # The diagonal is not part of the prediction task
+    # np.fill_diagonal(predicted_graph, 0)
+    # np.fill_diagonal(true_graph, 0)
+    
+    # Flatten both graphs to treat this as a binary classification problem
+    true_flat = true_graph.flatten()
+    pred_flat = predicted_graph.flatten()
+    
+    return f1_score(true_flat, pred_flat)
+
+def main():
+    """Main script to run the training and evaluation pipeline."""
+    
+    train_config = TrainConfig()
+    
+    # Check if the data directory exists
+    if not os.path.isdir(train_config.data_directory):
+        print(f"Error: Data directory '{train_config.data_directory}' not found.")
+        print(f"Please run the data generation script for '{train_config.data_directory}' first.")
+        return
+
+    print(f"🚀 Starting training pipeline for '{train_config.data_directory}' data.")
+    
+    # Get sorted list of agent directories
+    agent_dirs = sorted(
+        [d for d in os.listdir(train_config.data_directory) if d.startswith("agents_")],
+        key=lambda x: int(x.split('_')[1])
+    )
+
+    for agent_dir_name in agent_dirs:
+        agent_dir_path = os.path.join(train_config.data_directory, agent_dir_name)
+        
+        all_results_for_agent = []
+        
+        graph_files = sorted([f for f in os.listdir(agent_dir_path) if f.endswith(".json")])
+        
+        print(f"\nProcessing {len(graph_files)} graphs for {agent_dir_name}...")
+        
+        for graph_file_name in tqdm(graph_files, desc=f"Training on {agent_dir_name}"):
+            file_path = os.path.join(agent_dir_path, graph_file_name)
+            
+            with open(file_path, 'r') as f:
+                data = json.load(f)
+
+            # 1. Load and Prepare Data
+            trajectories = np.array(data['trajectories'])
+            s, l, n = trajectories.shape
+            # trajectories = trajectories.T
+            # np.random.shuffle(trajectories)
+            # trajectories = np.random.shuffle(trajectories)
+            true_graph = np.array(data['adjacency_matrix'])
+            inputs, targets = prepare_data_for_model(trajectories, train_config.batch_size)
+            
+            # 2. Configure Model
+            num_agents = trajectories.shape[-1]
+            model_config = ModelConfig(
+            )
+
+            model_config.num_agents=num_agents
+            model_config.input_dim=1 # Each agent has a single state value at time t
+            model_config.output_dim=1
+            model_config.embedding_dim=32 
+            
+            # 3. Train the Model
+            # This relies on the modified train_model that returns final params
+            final_params, train_logs = train_model(
+                config=model_config,
+                inputs=inputs,
+                targets=targets,
+                true_graph=true_graph,
+                train_config=train_config
+            )
+
+            # 4. Evaluate
+            f1 = calculate_f1_score(
+                final_params, 
+                model_config, 
+                true_graph, 
+                train_config.f1_threshold
+            )
+            
+            loss_history_serializable = {
+                epoch: [loss.item() for loss in losses] 
+                for epoch, losses in train_logs['loss_history'].items()
+            }
+
+            random_id = str(uuid.uuid4())
+
+            # 5. Log Results
+            result_log = {
+                # "model_name": random_id,
+                "source_file": graph_file_name,
+                "graph_metrics": data['graph_metrics'],
+                "f1_score": f1,
+                "training_loss_history": loss_history_serializable,
+                "config": {
+                    # Manually create the dictionary for the model config
+                    "model": {
+                        "num_agents": model_config.num_agents,
+                        "input_dim": model_config.input_dim,
+                        "output_dim": model_config.output_dim,
+                        "embedding_dim": model_config.embedding_dim
+                    },
+                    # This is correct because TrainConfig is a dataclass
+                    "training": vars(train_config)
+                }
+            }
+
+            result_final_params = final_params
+            
+
+            all_results_for_agent.append(result_log)
+
+        # 6. Save aggregated results for this agent count
+        results_dir = os.path.join(agent_dir_path, "results")
+        os.makedirs(results_dir, exist_ok=True)
+        
+        output_file = os.path.join(results_dir, "summary_results.json")
+        with open(output_file, 'w') as f:
+            json.dump(all_results_for_agent, f, indent=2)
+
+
+        model_path = os.path.join(results_dir, "model_params")
+        os.makedirs(model_path, exist_ok=True)
+        with open(os.path.join(model_path,"model_params" + ".pkl"), "wb") as f:
+            pickle.dump(final_params, f)
+            
+        print(f"✅ Results for {agent_dir_name} saved to {output_file}")
+        
+    print("\n🎉 Pipeline finished successfully!")
+
+if __name__ == "__main__":
+    main()

train_and_eval_w_noise.py

@@ -0,0 +1,217 @@
+import os
+import json
+import jax
+import jax.numpy as jnp
+import numpy as np
+from dataclasses import dataclass, field, asdict
+from enum import Enum
+from tqdm import tqdm
+from sklearn.metrics import f1_score
+
+# Import from your existing model file
+from model import ModelConfig, train_model, get_attention_fn
+
+# --- MODIFICATION: Enums for clarity and safety ---
+class DataSource(Enum):
+    CONSENSUS = "consensus"
+    KURAMOTO = "kuramoto"
+
+class NoiseType(Enum):
+    NONE = "none"
+    NORMAL = "normal"
+    UNIFORM = "uniform"
+
+# --- MODIFICATION: Updated TrainConfig ---
+@dataclass
+class TrainConfig:
+    """Configuration for the training process."""
+    learning_rate: float = 1e-3
+    epochs: int = 50
+    batch_size: int = 64
+    verbose: bool = False
+    log: bool = True
+    log_epoch_interval: int = 10
+    
+    # Data and Noise parameters
+    data_source: DataSource = DataSource.CONSENSUS
+    noise_type: NoiseType = NoiseType.NORMAL
+    noise_level: float = 0.1  # Stddev for Normal, half-width for Uniform
+
+    # Evaluation parameter
+    f1_threshold: float = 0.5
+    
+    @property
+    def source_data_directory(self) -> str:
+        """The directory where the source dataset is located."""
+        return f"{self.data_source.value}_dataset"
+
+    @property
+    def results_directory_name(self) -> str:
+        """Generates a unique output directory name for separate logging."""
+        if self.noise_type == NoiseType.NONE or self.noise_level == 0:
+            return f"results_noiseless"
+        return f"results_noise_{self.noise_type.value}_{self.noise_level}"
+
+# --- MODIFICATION: Updated data prep function to add noise ---
+def prepare_data_for_model(
+    trajectories: np.ndarray, 
+    key: jax.Array, 
+    train_config: TrainConfig
+) -> tuple[np.ndarray, np.ndarray]:
+    """
+    Converts trajectories to input-output pairs and adds noise to the inputs.
+    """
+    all_inputs, all_targets = [], []
+    num_sims, num_timesteps, num_agents = trajectories.shape
+    
+    for sim_idx in range(num_sims):
+        all_inputs.append(trajectories[sim_idx, :-1, :])
+        all_targets.append(trajectories[sim_idx, 1:, :])
+        
+    full_dataset_inputs = np.concatenate(all_inputs, axis=0)
+    full_dataset_targets = np.concatenate(all_targets, axis=0)
+    
+    # --- NOISE INJECTION BLOCK ---
+    if train_config.noise_type != NoiseType.NONE and train_config.noise_level > 0:
+        noise_shape = full_dataset_inputs.shape
+        if train_config.noise_type == NoiseType.NORMAL:
+            noise = jax.random.normal(key, noise_shape) * train_config.noise_level
+        elif train_config.noise_type == NoiseType.UNIFORM:
+            noise = jax.random.uniform(
+                key, noise_shape, 
+                minval=-train_config.noise_level, 
+                maxval=train_config.noise_level
+            )
+        full_dataset_inputs += np.array(noise) # Add noise to inputs
+    # --- END NOISE BLOCK ---
+
+    full_dataset_inputs = np.expand_dims(full_dataset_inputs, axis=-1)
+    full_dataset_targets = np.expand_dims(full_dataset_targets, axis=-1)
+    
+    num_samples = full_dataset_inputs.shape[0]
+    num_batches = num_samples // train_config.batch_size
+    
+    truncated_inputs = full_dataset_inputs[:num_batches * train_config.batch_size]
+    truncated_targets = full_dataset_targets[:num_batches * train_config.batch_size]
+    
+    batched_inputs = truncated_inputs.reshape(num_batches, train_config.batch_size, num_agents, 1)
+    batched_targets = truncated_targets.reshape(num_batches, train_config.batch_size, num_agents, 1)
+    
+    return batched_inputs, batched_targets
+
+def calculate_f1_score(
+    params: dict, 
+    model_config: ModelConfig, 
+    true_graph: np.ndarray, 
+    threshold: float
+) -> float:
+    """Extracts the learned graph and computes the F1 score."""
+    learned_scores = np.array(get_attention_fn(params, model_config))
+    flat_scores = learned_scores.flatten()
+    min_s, max_s = flat_scores.min(), flat_scores.max()
+    if max_s > min_s:
+        learned_scores = (learned_scores - min_s) / (max_s - min_s)
+    predicted_graph = (learned_scores > threshold).astype(int)
+    
+    np.fill_diagonal(predicted_graph, 0)
+    np.fill_diagonal(true_graph, 0)
+    
+    return f1_score(true_graph.flatten(), predicted_graph.flatten())
+
+def main():
+    """Main script to run the training and evaluation pipeline."""
+    
+    # Configure your training run here
+    train_config = TrainConfig(
+        noise_type=NoiseType.NORMAL,
+        noise_level=0.1
+    )
+    
+    if not os.path.isdir(train_config.source_data_directory):
+        print(f"Error: Source data '{train_config.source_data_directory}' not found.")
+        return
+
+    print(f"🚀 Starting training pipeline for '{train_config.data_source.value}' data.")
+    print(f"Noise Configuration: type={train_config.noise_type.value}, level={train_config.noise_level}")
+
+    # --- MODIFICATION: Main JAX key for noise generation ---
+    main_key = jax.random.PRNGKey(42)
+
+    agent_dirs = sorted(
+        [d for d in os.listdir(train_config.source_data_directory) if d.startswith("agents_")],
+        key=lambda x: int(x.split('_')[1])
+    )
+
+    for agent_dir_name in agent_dirs:
+        agent_dir_path = os.path.join(train_config.source_data_directory, agent_dir_name)
+        all_results_for_agent = []
+        graph_files = sorted([f for f in os.listdir(agent_dir_path) if f.endswith(".json")])
+        
+        print(f"\nProcessing {len(graph_files)} graphs for {agent_dir_name}...")
+        
+        for graph_file_name in tqdm(graph_files, desc=f"Training on {agent_dir_name}"):
+            file_path = os.path.join(agent_dir_path, graph_file_name)
+            
+            with open(file_path, 'r') as f:
+                data = json.load(f)
+
+            main_key, data_key = jax.random.split(main_key)
+            trajectories = np.array(data['trajectories'])
+            true_graph = np.array(data['adjacency_matrix'])
+            
+            # --- MODIFICATION: Pass key and config to data prep ---
+            inputs, targets = prepare_data_for_model(trajectories, data_key, train_config)
+            
+            num_agents = int(trajectories.shape[-1])
+            model_config = ModelConfig()
+            model_config.num_agents = num_agents
+            model_config.input_dim = 1
+            model_config.output_dim = 1
+            model_config.embedding_dim = 32
+            
+            final_params, train_logs = train_model(
+                config=model_config,
+                inputs=inputs,
+                targets=targets,
+                true_graph=true_graph,
+                train_config=train_config
+            )
+
+            f1 = calculate_f1_score(final_params, model_config, true_graph, train_config.f1_threshold)
+            
+            loss_history_serializable = {
+                epoch: [loss.item() for loss in losses] 
+                for epoch, losses in train_logs['loss_history'].items()
+            }
+            
+            result_log = {
+                "source_file": graph_file_name,
+                "graph_metrics": data['graph_metrics'],
+                "f1_score": f1,
+                "training_loss_history": loss_history_serializable,
+                "config": {
+                    "model": {
+                        "num_agents": model_config.num_agents,
+                        "input_dim": model_config.input_dim,
+                        "output_dim": model_config.output_dim,
+                        "embedding_dim": model_config.embedding_dim
+                    },
+                    "training": asdict(train_config)
+                }
+            }
+            all_results_for_agent.append(result_log)
+
+        # --- MODIFICATION: Save to a separate results directory ---
+        results_dir = os.path.join(agent_dir_path, train_config.results_directory_name)
+        os.makedirs(results_dir, exist_ok=True)
+        
+        output_file = os.path.join(results_dir, "summary_results.json")
+        with open(output_file, 'w') as f:
+            json.dump(all_results_for_agent, f, indent=2)
+            
+        print(f"✅ Results for {agent_dir_name} saved to {output_file}")
+        
+    print("\n🎉 Pipeline finished successfully!")
+
+if __name__ == "__main__":
+    main()

uv.lock

@@ -6,6 +6,15 @@ resolution-markers = [
     "python_full_version < '3.13'",
 ]
 
+[[package]]
+name = "absl-py"
+version = "2.3.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/10/2a/c93173ffa1b39c1d0395b7e842bbdc62e556ca9d8d3b5572926f3e4ca752/absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9", size = 116588, upload-time = "2025-07-03T09:31:44.05Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/8f/aa/ba0014cc4659328dc818a28827be78e6d97312ab0cb98105a770924dc11e/absl_py-2.3.1-py3-none-any.whl", hash = "sha256:eeecf07f0c2a93ace0772c92e596ace6d3d3996c042b2128459aaae2a76de11d", size = 135811, upload-time = "2025-07-03T09:31:42.253Z" },
+]
+
 [[package]]
 name = "anyio"
 version = "4.9.0"
@@ -218,6 +227,24 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/20/94/c5790835a017658cbfabd07f3bfb549140c3ac458cfc196323996b10095a/charset_normalizer-3.4.2-py3-none-any.whl", hash = "sha256:7f56930ab0abd1c45cd15be65cc741c28b1c9a34876ce8c17a2fa107810c0af0", size = 52626, upload-time = "2025-05-02T08:34:40.053Z" },
 ]
 
+[[package]]
+name = "chex"
+version = "0.1.90"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "absl-py" },
+    { name = "jax" },
+    { name = "jaxlib" },
+    { name = "numpy" },
+    { name = "setuptools" },
+    { name = "toolz" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/77/70/53c7d404ce9e2a94009aea7f77ef6e392f6740e071c62683a506647c520f/chex-0.1.90.tar.gz", hash = "sha256:d3c375aeb6154b08f1cccd2bee4ed83659ee2198a6acf1160d2fe2e4a6c87b5c", size = 92363, upload-time = "2025-07-23T19:50:47.945Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/6f/3d/46bb04776c465cea2dd8aa2d4b61ab610b707f798f47838ef7e6105b025c/chex-0.1.90-py3-none-any.whl", hash = "sha256:fce3de82588f72d4796e545e574a433aa29229cbdcf792555e41bead24b704ae", size = 101047, upload-time = "2025-07-23T19:50:46.603Z" },
+]
+
 [[package]]
 name = "colorama"
 version = "0.4.6"
@@ -386,6 +413,9 @@ dependencies = [
     { name = "jax", extra = ["cuda12"] },
     { name = "jupyter" },
     { name = "matplotlib" },
+    { name = "networkx" },
+    { name = "optax" },
+    { name = "scikit-learn" },
     { name = "seaborn" },
     { name = "tqdm" },
 ]
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