Sparcification_GCN_Heatmap.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPeLFFFwBlpVaSSi5b+LwRj",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MehrdadJalali-KIT/InverseLinkPredcition/blob/main/Sparcification_GCN_Heatmap.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oPrMetX1k7D-",
        "outputId": "617eeb54-3d39-4a8e-9529-4a9698de6d7d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "# Mount drive\n",
        "from google.colab import drive\n",
        "import os\n",
        "\n",
        "drive.mount('/content/drive')\n",
        "# Change working path\n",
        "os.chdir('/content/drive/MyDrive/Research/MOF/InverseLinkPredcition')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install rdkit"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lqCnJl0rmB0j",
        "outputId": "a41a5227-dc46-4b38-f403-b4bb5c38ba27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting rdkit\n",
            "  Downloading rdkit-2023.9.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (34.4 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m34.4/34.4 MB\u001b[0m \u001b[31m28.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from rdkit) (1.25.2)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from rdkit) (9.4.0)\n",
            "Installing collected packages: rdkit\n",
            "Successfully installed rdkit-2023.9.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import networkx as nx\n",
        "from rdkit import Chem\n",
        "from rdkit.Chem import AllChem\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from tensorflow.keras.layers import Input, Dense, Dropout, concatenate\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.regularizers import l2\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras.losses import CategoricalCrossentropy\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import random\n",
        "import tensorflow as tf\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from tensorflow.keras.models import load_model\n",
        "from tensorflow.keras import models, layers, regularizers\n",
        "\n",
        "\n",
        "\n",
        "np.random.seed(56)\n",
        "random.seed(56)\n",
        "tf.random.set_seed\n",
        "\n",
        "def plot_confusion_matrix(y_true, y_pred, classes):\n",
        "    # Compute confusion matrix\n",
        "    cm = confusion_matrix(y_true, y_pred)\n",
        "\n",
        "    # Plot confusion matrix\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=classes, yticklabels=classes)\n",
        "    plt.title('Confusion Matrix')\n",
        "    plt.xlabel('Predicted Labels')\n",
        "    plt.ylabel('True Labels')\n",
        "    plt.show()\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_loss_accuracy(history):\n",
        "    # Extract loss and accuracy from history\n",
        "    loss = history.history['loss']\n",
        "    val_loss = history.history.get('val_loss', None)\n",
        "    accuracy = history.history['accuracy']\n",
        "    val_accuracy = history.history.get('val_accuracy', None)\n",
        "    epochs = range(1, len(loss) + 1)\n",
        "\n",
        "    # Plot loss\n",
        "    plt.figure(figsize=(10, 8))\n",
        "    sns.heatmap(adjacency_matrix, annot=False, cmap='YlGnBu', cbar_kws={'label': 'Connection Strength'})\n",
        "    plt.title('Heatmap of Node Connectivity')\n",
        "    plt.xlabel('Node Index')\n",
        "    plt.ylabel('Node Index')\n",
        "    plt.show()\n",
        "\n",
        "    # Plot accuracy\n",
        "    plt.figure(figsize=(10, 5))\n",
        "    plt.plot(epochs, accuracy, 'darkblue', label='Training accuracy')  # Change color to dark blue\n",
        "    if val_accuracy:\n",
        "        plt.plot(epochs, val_accuracy, 'lightblue', label='Validation accuracy')  # Change color to light blue\n",
        "        plt.fill_between(epochs, np.array(val_accuracy) - np.std(val_accuracy), np.array(val_accuracy) + np.std(val_accuracy), color='lightblue', alpha=0.3)  # Fill between mean - std and mean + std\n",
        "    plt.title('Training and validation accuracy')\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.ylabel('Accuracy')\n",
        "    plt.legend()\n",
        "    plt.grid(True)\n",
        "\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "\n",
        "# Function to train and evaluate different classifiers\n",
        "def train_and_evaluate_classifier(classifier, train_features, train_labels, test_features, test_labels):\n",
        "    classifier.fit(train_features, train_labels)\n",
        "    accuracy = classifier.score(test_features, test_labels)\n",
        "    return accuracy\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import networkx as nx\n",
        "import numpy as np\n",
        "\n",
        "def preprocess_graph(graph, features):\n",
        "    # Determine the dimensionality of the feature vectors\n",
        "    feature_dimension = features.shape[1]\n",
        "\n",
        "    # Convert the graph to an adjacency matrix\n",
        "    adjacency_matrix = nx.adjacency_matrix(graph).toarray()\n",
        "\n",
        "  # Create a custom colormap, switching to 'viridis'\n",
        "    # cmap = sns.color_palette(\"viridis\", as_cmap=True)\n",
        "\n",
        "    # # Improve figure size and resolution\n",
        "    # plt.figure(figsize=(12, 10), dpi=300)  # Larger figure size and higher DPI for clarity\n",
        "\n",
        "    # # Draw the heatmap using the new colormap\n",
        "    # sns.heatmap(adjacency_matrix, annot=False, cmap=cmap, cbar_kws={'label': 'Connection Strength'})\n",
        "\n",
        "    # # Set the aspect of the plot to be equal\n",
        "    # plt.gca().set_aspect('equal', adjustable='box')\n",
        "\n",
        "    # # Update the plot title and labels with a larger font size for clarity\n",
        "    # plt.title('Heatmap of Node Connectivity', fontsize=16)\n",
        "    # plt.xlabel('Node Index', fontsize=14)\n",
        "    # plt.ylabel('Node Index', fontsize=14)\n",
        "\n",
        "    # # Use plt.tight_layout() to ensure the labels and color bar fit well in the plot\n",
        "    # plt.tight_layout()\n",
        "\n",
        "    # # Display the plot\n",
        "    # plt.show()\n",
        "\n",
        "    # Initialize an empty list to store feature vectors\n",
        "    feature_vectors = []\n",
        "\n",
        "    # Create a mapping from node labels to integer indices\n",
        "    node_to_index = {node: index for index, node in enumerate(graph.nodes())}\n",
        "\n",
        "    # Iterate over nodes in the graph\n",
        "    for node in graph.nodes():\n",
        "        # Get the integer index corresponding to the node label\n",
        "        node_index = node_to_index[node]\n",
        "        # Check if the node index is valid\n",
        "        if node_index < len(features):\n",
        "            # Append the feature vector corresponding to the node index\n",
        "            feature_vectors.append(features[node_index])\n",
        "        else:\n",
        "            # If the node index is out of range, assign a default feature vector\n",
        "            feature_vectors.append(np.zeros((feature_dimension,)))\n",
        "\n",
        "    # Convert the list of feature vectors to a numpy array\n",
        "    feature_matrix = np.array(feature_vectors)\n",
        "\n",
        "    return adjacency_matrix, feature_matrix\n",
        "\n",
        "\n",
        "\n",
        "def build_gcn_model(input_shape_feature, input_shape_adjacency, num_classes):\n",
        "    # Define input layers\n",
        "    x_inp_feature = Input(shape=(input_shape_feature,))\n",
        "    x_inp_adjacency = Input(shape=(input_shape_adjacency,))\n",
        "\n",
        "    # Define GCN layers for feature input\n",
        "    x_feature = Dense(128, activation='relu', kernel_regularizer=l2(0.001))(x_inp_feature)\n",
        "    x_feature = Dropout(0.5)(x_feature)\n",
        "    x_feature = Dense(64, activation='relu', kernel_regularizer=l2(0.001))(x_feature)\n",
        "    x_feature = Dropout(0.5)(x_feature)\n",
        "\n",
        "    # Define GCN layers for adjacency input\n",
        "    x_adjacency = Dense(128, activation='relu', kernel_regularizer=l2(0.001))(x_inp_adjacency)\n",
        "    x_adjacency = Dropout(0.5)(x_adjacency)\n",
        "    x_adjacency = Dense(64, activation='relu', kernel_regularizer=l2(0.001))(x_adjacency)\n",
        "    x_adjacency = Dropout(0.5)(x_adjacency)\n",
        "\n",
        "    # Concatenate feature and adjacency outputs\n",
        "    x = concatenate([x_feature, x_adjacency])\n",
        "\n",
        "    # Adding more hidden layers for better representation learning\n",
        "    x = Dense(64, activation='relu', kernel_regularizer=l2(0.001))(x)\n",
        "    x = Dropout(0.5)(x)\n",
        "\n",
        "    # Output layer\n",
        "    output = Dense(num_classes, activation='softmax')(x)\n",
        "\n",
        "    # Create model\n",
        "    model = Model(inputs=[x_inp_feature, x_inp_adjacency], outputs=output)\n",
        "\n",
        "    # Using a smaller learning rate\n",
        "    optimizer = Adam(learning_rate=0.001)\n",
        "\n",
        "    # Compile model\n",
        "    model.compile(optimizer=optimizer, loss=CategoricalCrossentropy(), metrics=['accuracy'])\n",
        "\n",
        "    return model\n",
        "\n",
        "def build_feedforward_model(input_shape, num_classes):\n",
        "    model = models.Sequential([\n",
        "        layers.Input(shape=(input_shape,)),\n",
        "        layers.Dense(64, activation='relu', kernel_regularizer=regularizers.l2(0.01)),\n",
        "        layers.Dropout(0.5),\n",
        "        layers.Dense(32, activation='relu', kernel_regularizer=regularizers.l2(0.01)),\n",
        "        layers.Dense(num_classes, activation='softmax')\n",
        "    ])\n",
        "    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "    return model\n",
        "\n",
        "\n",
        "\n",
        "def train_gcn_model(model, adjacency_matrix, feature_matrix, labels, epochs, batch_size):\n",
        "    if model is not None and adjacency_matrix is not None and feature_matrix is not None and labels is not None:\n",
        "\n",
        "        history=model.fit([feature_matrix, adjacency_matrix], labels, epochs=epochs, batch_size=batch_size, validation_split=0.2)\n",
        "        return history\n",
        "    else:\n",
        "        print(\"Error: One or more input arguments to train_gcn_model is None.\")\n",
        "def label_encode_metal_names(metal_names):\n",
        "    \"\"\"Encodes metal names as integers.\"\"\"\n",
        "    metal_dict = {metal: idx for idx, metal in enumerate(np.unique(metal_names))}\n",
        "    return np.array([metal_dict[metal] for metal in metal_names])\n",
        "\n",
        "\n",
        "def generate_fingerprint(smiles):\n",
        "    \"\"\"Generates a molecular fingerprint given a SMILES string.\"\"\"\n",
        "    try:\n",
        "        mol = Chem.MolFromSmiles(smiles)\n",
        "        if mol is None:\n",
        "            return np.zeros((1024,), dtype=float)  # Return an array of zeros if molecule can't be parsed\n",
        "        return np.array(AllChem.GetMorganFingerprintAsBitVect(mol, 2, nBits=1024), dtype=float)\n",
        "    except Exception as e:\n",
        "        print(f\"SMILES Parse Error: {e}\")\n",
        "        return np.zeros((1024,), dtype=float)  # Return an array of zeros in case of an error\n",
        "\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    # Define the filenames\n",
        "    # edges_list_filename = 'edges_list_0.8_Full.csv' , edges_list_filename ='sparsified_graph_edges_0.98.csv'\n",
        "    edges_list_filename ='sparsified_graph_edges_0.98.csv'\n",
        "    summary_data_filename = '1M1L3D_summary.csv'\n",
        "\n",
        "    # Load the edge list from the CSV file\n",
        "    edges_list = pd.read_csv(edges_list_filename, header=None, names=['source', 'target', 'weight'], delimiter=' ')\n",
        "\n",
        "    # Extract unique node labels from the edge list\n",
        "  # Load summary data\n",
        "    summary_data = pd.read_csv(summary_data_filename)\n",
        "\n",
        "    node_labels_source = edges_list['source'].astype(str).unique()\n",
        "\n",
        "    node_labels_target = edges_list['target'].astype(str).unique()\n",
        "\n",
        "    node_labels = np.concatenate((node_labels_source, node_labels_target))\n",
        "    node_labels = np.unique(node_labels)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "    # Remove any duplicate labels\n",
        "    node_labels = list(set(node_labels))\n",
        "\n",
        "    # Debug print statement\n",
        "    print(\"Unique node labels:\", len(node_labels))\n",
        "\n",
        "\n",
        "    # Filter summary data based on node labels\n",
        "    summary_data_filtered = summary_data[summary_data['refcode'].isin(node_labels)]  # Replace '<column_name>' with the actual column name containing node labels\n",
        "\n",
        "       # Debug print statement to check contents of filtered summary data\n",
        "    print(\"Filtered summary data:\\n\", len(summary_data_filtered))\n",
        "\n",
        "    # Extract features and labels\n",
        "    if not summary_data_filtered.empty:\n",
        "        linker_smiles = summary_data_filtered['linker SMILES']\n",
        "        if not linker_smiles.empty:\n",
        "            linker_features = np.stack(linker_smiles.dropna().apply(generate_fingerprint).values)\n",
        "            metal_names = summary_data_filtered['metal']\n",
        "            metal_features = label_encode_metal_names(metal_names).reshape(-1, 1)\n",
        "            other_features = summary_data_filtered[['Largest Cavity Diameter', 'Largest Free Sphere']].values.astype(\n",
        "                'float32')\n",
        "            features = np.concatenate((linker_features, metal_features, other_features), axis=1)\n",
        "            summary_data_filtered['PLD_category'] = pd.cut(summary_data_filtered['Pore Limiting Diameter'], bins=[-np.inf, 2.4, 4.4, 5.9, np.inf], labels=['nonporous', 'small pore', 'medium pore', 'large pore'])\n",
        "            labels = pd.get_dummies(summary_data_filtered['PLD_category']).values\n",
        "            print ('Number of Labels', len(labels))\n",
        "            # Load the sparsified graph\n",
        "            graph = nx.read_weighted_edgelist(edges_list_filename)\n",
        "\n",
        "            # Print the number of nodes and edges in the graph\n",
        "            print(\"Number of nodes:\", graph.number_of_nodes())\n",
        "            print(\"Number of edges:\", graph.number_of_edges())\n",
        "\n",
        "            # Preprocess the graph data\n",
        "            adjacency_matrix, feature_matrix = preprocess_graph(graph, features)\n",
        "\n",
        "          # Provide the number of classes\n",
        "            num_classes = labels.shape[1]\n",
        "\n",
        "            # Build the GCN model\n",
        "            gcn_model = build_gcn_model(feature_matrix.shape[1], adjacency_matrix.shape[1], num_classes)\n",
        "\n",
        "\n",
        "            # Train the GCN model\n",
        "            history=train_gcn_model(gcn_model, adjacency_matrix, feature_matrix, labels, epochs=40, batch_size=32)\n",
        "            # Save the trained GCN model\n",
        "            gcn_model.save(\"gcn_model_Original.h5\")\n",
        "\n",
        "            # Save the adjacency matrix\n",
        "            np.save(\"adjacency_matrix_Original.npy\", adjacency_matrix)\n",
        "            # Calculate accuracy\n",
        "            test_loss, test_accuracy = gcn_model.evaluate([feature_matrix, adjacency_matrix], labels, verbose=0)\n",
        "            print(f'Test Accuracy: {test_accuracy}')\n",
        "\n",
        "            # Plot loss and accuracy\n",
        "            plot_loss_accuracy(history)\n",
        "            y_true = np.argmax(labels, axis=1)  # Convert one-hot encoded labels to integer labels\n",
        "            y_pred = np.argmax(gcn_model.predict([feature_matrix, adjacency_matrix]), axis=1)  # Get predicted labels\n",
        "            classes = ['nonporous', 'small pore', 'medium pore', 'large pore']  # List of class labels\n",
        "            plot_confusion_matrix(y_true, y_pred, classes)\n",
        "\n",
        "\n",
        "\n",
        "            # Split data into training and testing sets\n",
        "            train_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size=0.2, random_state=42)\n",
        "            # Train and evaluate Gradient Boosted Trees\n",
        "\n",
        "\n",
        "        else:\n",
        "            print(\"Error: linker_smiles column is empty.\")\n",
        "    else:\n",
        "        print(\"Error: summary_data_filtered DataFrame is empty.\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "R6hceiCjlIto",
        "outputId": "c8819017-cc76-46ab-8c5a-8827855fbc75"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unique node labels: 2112\n",
            "Filtered summary data:\n",
            " 2112\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-4-7a7fafaf3f03>:270: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  summary_data_filtered['PLD_category'] = pd.cut(summary_data_filtered['Pore Limiting Diameter'], bins=[-np.inf, 2.4, 4.4, 5.9, np.inf], labels=['nonporous', 'small pore', 'medium pore', 'large pore'])\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Labels 2112\n",
            "Number of nodes: 2112\n",
            "Number of edges: 1883\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 3600x3000 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/40\n",
            "53/53 [==============================] - 2s 13ms/step - loss: 1.8249 - accuracy: 0.3618 - val_loss: 1.5571 - val_accuracy: 0.4728\n",
            "Epoch 2/40\n",
            "53/53 [==============================] - 1s 11ms/step - loss: 1.4186 - accuracy: 0.4950 - val_loss: 1.3802 - val_accuracy: 0.5177\n",
            "Epoch 3/40\n",
            "43/53 [=======================>......] - ETA: 0s - loss: 1.2296 - accuracy: 0.5669"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-4-7a7fafaf3f03>\u001b[0m in \u001b[0;36m<cell line: 223>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    289\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    290\u001b[0m             \u001b[0;31m# Train the GCN model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 291\u001b[0;31m             \u001b[0mhistory\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain_gcn_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgcn_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madjacency_matrix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeature_matrix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m40\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    292\u001b[0m             \u001b[0;31m# Save the trained GCN model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    293\u001b[0m             \u001b[0mgcn_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"gcn_model_Original.h5\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-4-7a7fafaf3f03>\u001b[0m in \u001b[0;36mtrain_gcn_model\u001b[0;34m(model, adjacency_matrix, feature_matrix, labels, epochs, batch_size)\u001b[0m\n\u001b[1;32m    199\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0madjacency_matrix\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeature_matrix\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    200\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 201\u001b[0;31m         \u001b[0mhistory\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfeature_matrix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madjacency_matrix\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_split\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    202\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mhistory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    203\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m             \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1796\u001b[0m                 \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_epoch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1797\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcatch_stop_iteration\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1798\u001b[0;31m                     \u001b[0;32mfor\u001b[0m \u001b[0mstep\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1799\u001b[0m                         with tf.profiler.experimental.Trace(\n\u001b[1;32m   1800\u001b[0m                             \u001b[0;34m\"train\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/engine/data_adapter.py\u001b[0m in \u001b[0;36msteps\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1409\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_insufficient_data\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# Set by `catch_stop_iteration`.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1410\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1411\u001b[0;31m             \u001b[0moriginal_spe\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_steps_per_execution\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1412\u001b[0m             can_run_full_execution = (\n\u001b[1;32m   1413\u001b[0m                 \u001b[0moriginal_spe\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/ops/resource_variable_ops.py\u001b[0m in \u001b[0;36mnumpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    687\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    688\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecuting_eagerly\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 689\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    690\u001b[0m     raise NotImplementedError(\n\u001b[1;32m    691\u001b[0m         \"numpy() is only available when eager execution is enabled.\")\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/ops/resource_variable_ops.py\u001b[0m in \u001b[0;36mread_value\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    836\u001b[0m       \u001b[0mThe\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mvariable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    837\u001b[0m     \"\"\"\n\u001b[0;32m--> 838\u001b[0;31m     \u001b[0;32mwith\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname_scope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Read\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    839\u001b[0m       \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_read_variable_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    840\u001b[0m     \u001b[0;31m# Return an identity so it can get placed on whatever device the context\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mname_scope\u001b[0;34m(name, default_name, values, skip_on_eager)\u001b[0m\n\u001b[1;32m   5432\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5433\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mskip_on_eager\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5434\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mNullContextmanager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5435\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5436\u001b[0m   \u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdefault_name\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    159\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mNullContextmanager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    160\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 161\u001b[0;31m   \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    162\u001b[0m     \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import networkx as nx\n",
        "from matplotlib.colors import LinearSegmentedColormap\n",
        "\n",
        "\n",
        "# Assuming 'graph' is a NetworkX graph object\n",
        "adjacency_matrix = np.load('adjacency_matrix_0.9.npy')\n",
        "\n",
        "# Create a colormap that goes from white to blue\n",
        "cmap = LinearSegmentedColormap.from_list('white_to_blue', ['white', 'blue'])\n",
        "\n",
        "# Improve figure size and resolution\n",
        "plt.figure(figsize=(12, 10))\n",
        "\n",
        "# Draw the heatmap with the new colormap\n",
        "sns.heatmap(adjacency_matrix, annot=False, cmap=cmap, cbar_kws={'label': 'Connection Strength'})\n",
        "\n",
        "# Set the aspect ratio to be equal for square cells\n",
        "plt.gca().set_aspect('equal', adjustable='box')\n",
        "\n",
        "# Improve the readability of the plot\n",
        "plt.title('Heatmap of Node Connectivity - Sparcification Threshold 0.9', fontsize=16)\n",
        "plt.xlabel('Node Index', fontsize=14)\n",
        "plt.ylabel('Node Index', fontsize=14)\n",
        "\n",
        "# Ensure that the plot fits neatly within the figure area\n",
        "plt.tight_layout()\n",
        "\n",
        "# Display the plot\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 879
        },
        "id": "Prh6BZ-HMG9G",
        "outputId": "6acf404d-2667-4c45-dafc-9c6f23e7c3b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "528D6gESUQm4"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}