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Updated data generation code

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lepton
2025-09-01 14:46:34 -04:00
parent d998f6de4c
commit e018238935
14 changed files with 709 additions and 123 deletions
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2
.gitignore vendored
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@@ -1,6 +1,6 @@
.venv
.env
__pycaches__
__pycache__
datasets/
temp.*
test.*

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__pycache__/config_.cpython-312.pyc
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__pycache__/generate_data_consensus.cpython-312.pyc
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19
config_.py
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@@ -9,7 +9,7 @@ class NoiseType(Enum):
class ModelConfig:
num_agents: int
embedding_dim: int = 16
embedding_dim: int = 64
input_dim: int = 1
output_dim: int = 1
@@ -19,15 +19,12 @@ class TrainConfig:
"""Configuration for the training process."""
learning_rate: float = 1e-3
epochs: int = 100
batch_size: int = 32
verbose: bool = False # Set to True to see epoch loss during training
log: bool = True
log_epoch_interval: int = 10
noise_type: NoiseType = NoiseType.NONE
noise_level: float = 0.01
# --- New parameters for this script ---
batch_size: int = 4096 * 4
verbose: bool = False # See loss during training
log: bool = True # Logs the attention every log interval
log_epoch_interval: int = 10 # How often you want to log intervals
noise_type: NoiseType = NoiseType.NONE # What kind of noise you want
noise_level: float = 0.00 # Stddev for Normal, half-width for Uniform
data_directory:str
# Threshold for converting attention scores to a binary graph
f1_threshold: float = -0.5
f1_threshold: float = -0.5 # Threshold for binary classification

81
generate_data_consensus.py
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@@ -8,7 +8,9 @@ import networkx as nx
import networkx.algorithms.community as nx_comm
from tqdm import tqdm
import sims
from dotenv import load_dotenv
load_dotenv()
def generate_connected_graph(rng: np.random.Generator, num_agents: int, graph_type: str) -> tuple[nx.Graph, str, np.ndarray]:
@@ -21,35 +23,34 @@ def generate_connected_graph(rng: np.random.Generator, num_agents: int, graph_ty
"""
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
k = 4
p = 0.5
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)
G = nx.barabasi_albert_graph(num_agents, 2, 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)
m = 2
p = 0.5
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
graph_matrix = nx.adjacency_matrix(G).todense()
for i in range(num_agents):
graph_matrix[i,i]=1
G = nx.Graph(graph_matrix)
return G, graph_type, graph_matrix
def calculate_graph_metrics(G: nx.Graph) -> dict:
"""
@@ -91,30 +92,30 @@ def calculate_graph_metrics(G: nx.Graph) -> dict:
# --- 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
# 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
# # 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
@@ -132,9 +133,9 @@ def main():
# --- 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_AGENT_COUNT = 80
GRAPHS_PER_GRAPH_ALGO = GRAPHS_PER_AGENT_COUNT // len(GRAPH_GEN_ALGOS)
SIMS_PER_GRAPH = 100
SIMS_PER_GRAPH = 400
OUTPUT_DIR = "datasets/consensus_dataset"
# --- Setup ---

4
generate_data_kuramoto.py
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@@ -82,9 +82,9 @@ def main():
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_AGENT_COUNT = 80
GRAPHS_PER_GRAPH_ALGO = GRAPHS_PER_AGENT_COUNT // len(GRAPH_GEN_ALGOS)
SIMS_PER_GRAPH = 100
SIMS_PER_GRAPH = 400
OUTPUT_DIR = "datasets/kuramoto_dataset"
# --- Setup ---

3
model.py
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@@ -46,7 +46,7 @@ def init_fn(key: jax.Array, config: ModelConfig):
def forward(params: dict, input_timesteps: jax.Array, config: ModelConfig):
"""
Model's forward function. Takes in the parameters and inptu timesteps, returns predictions
Model's forward function. Takes in the parameters and input timesteps, returns predictions
"""
batch_size, num_agents, _ = input_timesteps.shape
@@ -119,6 +119,7 @@ def train_model(config: ModelConfig, inputs: jax.Array, targets: jax.Array,
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)

88
plot_results.py
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@@ -10,7 +10,7 @@ from train_and_eval import calculate_f1_score
from sklearn.metrics import f1_score
if len(sys.argv) < 2:
data_dir = "datasets/consensus_dataset"
data_dir = "datasets/kuramoto_dataset"
else:
data_dir = "datasets/" + sys.argv[1]
@@ -22,53 +22,59 @@ for folder in tqdm(os.listdir(data_dir)):
folder_path = os.path.join(data_dir, folder)
# Load model config from summary json
with open(os.path.join(folder_path, "results/NoiseType.NONE", "summary_results.json"), "r") as f:
summary_results = json.load(f)
for noise_level in os.listdir(os.path.join(folder_path, "results/NoiseType.NONE")):
for i, graph in enumerate(os.listdir(folder_path)):
# train_summary_results
summ_results = summary_results[i-1]
# Load model config from summary json
with open(os.path.join(folder_path, "results/NoiseType.NONE", noise_level, "summary_results.json"), "r") as f:
summary_results = json.load(f)
if graph == "results": # ignore the result folder
for i, graph in enumerate(os.listdir(folder_path)):
# train_summary_results
summ_results = summary_results[i-1]
if graph == "results": # ignore the result folder
continue
graph_path = os.path.join(folder_path, graph)
# Load run data
with open(os.path.join(folder_path, graph), "r") as f:
run_data = json.load(f)
true_graph = np.array(run_data["adjacency_matrix"])
learned_graph = np.array(summ_results["raw_attention"])
predicted_graph = (learned_graph > THRESHOLD).astype(int)
true_flat = true_graph.flatten()
pred_flat = predicted_graph.flatten()
calc_f1_score = f1_score(true_flat, pred_flat)
datapoints[num_agents] = datapoints.get(num_agents, [])
datapoints[num_agents].append(calc_f1_score)
for key in datapoints.keys():
try:
datapoints[key] = sum(datapoints[key])/len(datapoints[key])
except:
continue
graph_path = os.path.join(folder_path, graph)
# Load run data
with open(os.path.join(folder_path, graph), "r") as f:
run_data = json.load(f)
x = []
y = []
true_graph = np.array(run_data["adjacency_matrix"])
learned_graph = np.array(summ_results["raw_attention"])
for item in datapoints.items():
x.append(item[0])
y.append(item[1])
predicted_graph = (learned_graph > THRESHOLD).astype(int)
true_flat = true_graph.flatten()
pred_flat = predicted_graph.flatten()
calc_f1_score = f1_score(true_flat, pred_flat)
datapoints[num_agents] = datapoints.get(num_agents, [])
datapoints[num_agents].append(calc_f1_score)
for key in datapoints.keys():
datapoints[key] = sum(datapoints[key])/len(datapoints[key])
x = []
y = []
for item in datapoints.items():
x.append(item[0])
y.append(item[1])
plt.plot(x, y)
plt.show()
plt.plot(x, y)
plt.show()

2
pyproject.toml
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@@ -12,6 +12,8 @@ dependencies = [
"matplotlib>=3.10.3",
"networkx>=3.5",
"optax>=0.2.5",
"pyqt6>=6.9.1",
"python-dotenv>=1.1.1",
"scikit-learn>=1.7.1",
"seaborn>=0.13.2",
"tqdm>=4.67.1",

521
test.ipynb
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File diff suppressed because one or more lines are too long

51
train_and_eval.py
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@@ -51,7 +51,7 @@ def prepare_data_for_model(key: jax.Array, trajectories: np.ndarray, train_confi
all_targets = all_targets[all_indices]
if train_config.noise_type != NoiseType.NONE and train_config.noise_level > 0:
noise_shape = full_dataset_inputs.shape
noise_shape = all_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:
@@ -60,18 +60,18 @@ def prepare_data_for_model(key: jax.Array, trajectories: np.ndarray, train_confi
minval=-train_config.noise_level,
maxval=train_config.noise_level
)
full_dataset_inputs += np.array(noise) # Add noise to inputs
all_inputs += np.array(noise) # Add noise to inputs
full_dataset_inputs = np.expand_dims(all_inputs, axis=-1)
all_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_samples = all_inputs.shape[0]
num_batches = num_samples // batch_size
# Truncate to full batches
truncated_inputs = full_dataset_inputs[:num_batches * batch_size]
truncated_inputs = all_inputs[:num_batches * batch_size]
truncated_targets = full_dataset_targets[:num_batches * batch_size]
# Reshape into batches
@@ -81,6 +81,40 @@ def prepare_data_for_model(key: jax.Array, trajectories: np.ndarray, train_confi
return batched_inputs, batched_targets
def f1_score_np(y_true: np.ndarray, y_pred: np.ndarray) -> float:
"""
Compute the F1 score between two numpy arrays.
Parameters
----------
y_true : np.ndarray
Ground truth (correct) labels.
y_pred : np.ndarray
Predicted labels.
Returns
-------
float
The F1 score.
"""
# Ensure binary arrays (0 or 1)
y_true = np.asarray(y_true).astype(int)
y_pred = np.asarray(y_pred).astype(int)
# Compute True Positives, False Positives, and False Negatives
tp = np.sum((y_true == 1) & (y_pred == 1))
fp = np.sum((y_true == 0) & (y_pred == 1))
fn = np.sum((y_true == 1) & (y_pred == 0))
# Precision and Recall
precision = tp / (tp + fp) if (tp + fp) > 0 else 0.0
recall = tp / (tp + fn) if (tp + fn) > 0 else 0.0
# F1 Score
if precision + recall == 0:
return 0.0
return 2 * (precision * recall) / (precision + recall)
def calculate_f1_score(
params: dict,
model_config: ModelConfig,
@@ -111,7 +145,7 @@ def calculate_f1_score(
true_flat = true_graph.flatten()
pred_flat = predicted_graph.flatten()
return f1_score(true_flat, pred_flat)
return f1_score_np(true_flat, pred_flat)
def main():
"""Main script to run the training and evaluation pipeline."""
@@ -125,7 +159,7 @@ def main():
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.")
print(f"Starting training pipeline for '{train_config.data_directory}' data.")
# Get sorted list of agent directories
agent_dirs = sorted(
@@ -146,7 +180,8 @@ def main():
os.makedirs(results_dir, exist_ok=True)
subdir = str(train_config.noise_type)
sub_results_dir = os.path.join(results_dir, subdir)
subsubdir = str(train_config.noise_level)
sub_results_dir = os.path.join(results_dir, subdir, subsubdir)
os.makedirs(sub_results_dir, exist_ok=True)
print(f"\nProcessing {len(graph_files)} graphs for {agent_dir_name}...")

61
uv.lock generated
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@@ -415,6 +415,8 @@ dependencies = [
{ name = "matplotlib" },
{ name = "networkx" },
{ name = "optax" },
{ name = "pyqt6" },
{ name = "python-dotenv" },
{ name = "scikit-learn" },
{ name = "seaborn" },
{ name = "tqdm" },
@@ -429,6 +431,8 @@ requires-dist = [
{ name = "matplotlib", specifier = ">=3.10.3" },
{ name = "networkx", specifier = ">=3.5" },
{ name = "optax", specifier = ">=0.2.5" },
{ name = "pyqt6", specifier = ">=6.9.1" },
{ name = "python-dotenv", specifier = ">=1.1.1" },
{ name = "scikit-learn", specifier = ">=1.7.1" },
{ name = "seaborn", specifier = ">=0.13.2" },
{ name = "tqdm", specifier = ">=4.67.1" },
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