from graph_universe import launch_ui
launch_ui()  # Opens browser, press Ctrl+C to stop

from graph_universe import GraphUniverse, GraphFamilyGenerator

# Create universe with 8 communities and 10-dimensional features
universe = GraphUniverse(K=8, edge_propensity_variance=0.3, feature_dim=10)

# Generate family with full parameter control
family = GraphFamilyGenerator(
    universe=universe,
    n_nodes_range=(35, 50),
    n_communities_range=(2, 6),
    homophily_range=(0.2, 0.8),
    avg_degree_range=(2.0, 10.0),
    power_law_exponent_range=(2.0, 5.0),
    degree_separation_range=(0.1, 0.7),
    seed=42
)

# Generate 30 graphs
family.generate_family(n_graphs=30, show_progress=True)

print(f"Generated {len(family.graphs)} graphs!")

# Convert to PyTorch Geometric format for training
pyg_graphs = family.to_pyg_graphs(task="community_detection")

import yaml
from graph_universe import GraphUniverseDataset

with open("config.yaml") as f:
    config = yaml.safe_load(f)

dataset = GraphUniverseDataset(root="./data", parameters=config)
print(f"Generated dataset with {len(dataset)} graphs!")

# Validate standard graph properties
family_properties = family.analyze_graph_family_properties()
for property_name in ['node_counts', 'avg_degrees', 'homophily_levels']:
    values = family_properties[property_name]
    print(f"{property_name}: mean={np.mean(values):.3f}")

# Analyze within-graph community signals (fits Random Forest per graph)
family_signals = family.analyze_graph_family_signals()
for signal in ['structure_signal', 'feature_signal', 'degree_signal']:
    values = family_signals[signal]
    print(f"{signal}: mean={np.mean(values):.3f}")

# Measure between-graph consistency
family_consistency = family.analyze_graph_family_consistency()
for metric in ['structure_consistency', 'feature_consistency', 'degree_consistency']:
    value = family_consistency[metric]
    print(f"{metric}: {value:.3f}")