Create 17_Graph2.py

pages/17_Graph2.py

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+import streamlit as st
+import tensorflow as tf
+import tensorflow_gnn as tfgnn
+from tensorflow_gnn.models import mt_albis
+import networkx as nx
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Set environment variable for legacy Keras
+import os
+os.environ['TF_USE_LEGACY_KERAS'] = '1'
+
+# Define the model function
+def model_fn(graph_tensor_spec: tfgnn.GraphTensorSpec):
+    graph = inputs = tf.keras.Input(type_spec=graph_tensor_spec)
+
+    # Encode input features to match the required output shape of 128
+    graph = tfgnn.keras.layers.MapFeatures(
+        node_sets_fn=lambda node_set, node_set_name: tf.keras.layers.Dense(128)(node_set['features'])
+    )(graph)
+
+    # For each round of message passing...
+    for _ in range(2):
+        # ... create and apply a Keras layer.
+        graph = mt_albis.MtAlbisGraphUpdate(
+            units=128, message_dim=64,
+            attention_type="none", simple_conv_reduce_type="mean",
+            normalization_type="layer", next_state_type="residual",
+            state_dropout_rate=0.2, l2_regularization=1e-5,
+            receiver_tag=tfgnn.TARGET  # Use TARGET instead of NODES
+        )(graph)
+
+    return tf.keras.Model(inputs, graph)
+
+# Function to create a sample graph with meaningful synthetic data
+def create_sample_graph():
+    num_nodes = 10
+    num_edges = 15
+
+    graph = nx.gnm_random_graph(num_nodes, num_edges, directed=True)
+
+    # Create synthetic features
+    years_published = np.random.randint(1990, 2022, size=num_nodes).astype(np.float32)
+    num_authors = np.random.randint(1, 10, size=num_nodes).astype(np.float32)
+    citation_weights = np.random.uniform(0.1, 5.0, size=num_edges).astype(np.float32)
+    
+    # Combine features into a single array per node
+    node_features = np.stack([years_published, num_authors], axis=-1)
+    edge_features = citation_weights.reshape(-1, 1)
+
+    graph_tensor = tfgnn.GraphTensor.from_pieces(
+        node_sets={
+            "papers": tfgnn.NodeSet.from_fields(
+                sizes=[num_nodes],
+                features={"features": tf.convert_to_tensor(node_features)}
+            )
+        },
+        edge_sets={
+            "cites": tfgnn.EdgeSet.from_fields(
+                sizes=[num_edges],
+                adjacency=tfgnn.Adjacency.from_indices(
+                    source=("papers", tf.constant([e[0] for e in graph.edges()], dtype=tf.int32)),
+                    target=("papers", tf.constant([e[1] for e in graph.edges()], dtype=tf.int32))
+                ),
+                features={"features": tf.convert_to_tensor(edge_features)}
+            )
+        }
+    )
+
+    return graph, graph_tensor
+
+# Streamlit app
+def main():
+    st.title("Graph Neural Network Architecture Visualization")
+
+    # Create sample graph
+    nx_graph, graph_tensor = create_sample_graph()
+
+    # Create and compile the model
+    model = model_fn(graph_tensor.spec)
+    model.compile(optimizer='adam', loss='binary_crossentropy')
+
+    # Display model summary
+    st.subheader("Model Summary")
+    model.summary(print_fn=lambda x: st.text(x))
+
+    # Visualize the graph
+    st.subheader("Sample Graph Visualization")
+    fig, ax = plt.subplots(figsize=(10, 8))
+    pos = nx.spring_layout(nx_graph)
+    nx.draw(nx_graph, pos, with_labels=True, node_color='lightblue',
+            node_size=500, arrowsize=20, ax=ax)
+    st.pyplot(fig)
+
+    # Display graph tensor info
+    st.subheader("Graph Tensor Information")
+    st.text(f"Number of nodes: {graph_tensor.node_sets['papers'].total_size}")
+    st.text(f"Number of edges: {graph_tensor.edge_sets['cites'].total_size}")
+    st.text(f"Node feature shape: {graph_tensor.node_sets['papers']['features'].shape}")
+    st.text(f"Edge feature shape: {graph_tensor.edge_sets['cites']['features'].shape}")
+
+    # Display sample node and edge features
+    st.subheader("Sample Node and Edge Features")
+    st.write("Node Features (Year Published, Number of Authors):")
+    st.write(node_features)
+    st.write("Edge Features (Citation Weight):")
+    st.write(edge_features)
+
+if __name__ == "__main__":
+    main()