CompareMLMethods_Sparcified_0_98.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyORgqXoOXWTkiCVDLBys7hR",
      "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/CompareMLMethods_Sparcified_0_98.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WyxOwNCZwyGF",
        "outputId": "ea3fab9b-fbe7-40b3-8c9a-3724dca294c8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\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": "a6M4Osh0w_Cq",
        "outputId": "7fed69e6-fce4-46d6-d7ee-327ede779567"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting rdkit\n",
            "  Downloading rdkit-2024.3.5-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (3.9 kB)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from rdkit) (1.26.4)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from rdkit) (9.4.0)\n",
            "Downloading rdkit-2024.3.5-cp310-cp310-manylinux_2_28_x86_64.whl (33.1 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m33.1/33.1 MB\u001b[0m \u001b[31m11.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: rdkit\n",
            "Successfully installed rdkit-2024.3.5\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 LabelEncoder\n",
        "from sklearn.preprocessing import LabelBinarizer\n",
        "from sklearn.metrics import roc_auc_score\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",
        "from statsmodels.stats.contingency_tables import mcnemar\n",
        "from sklearn.metrics import confusion_matrix, cohen_kappa_score, matthews_corrcoef, roc_auc_score\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",
        "import warnings\n",
        "from rdkit import RDLogger\n",
        "from tensorflow.keras import models, layers, regularizers\n",
        "from sklearn.metrics import accuracy_score\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "import time\n",
        "\n",
        "threshold=0.98\n",
        "\n",
        "# np.random.seed(56)\n",
        "# random.seed(56)\n",
        "# tf.random.set_seed\n",
        "\n",
        "# Suppress specific deprecation warnings\n",
        "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n",
        "\n",
        "# Additionally, suppress RDKit warnings globally\n",
        "RDLogger.DisableLog('rdApp.*')\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",
        "\n",
        "def plot_confusion_matrix(y_true, y_pred, classes):\n",
        "    # Compute confusion matrix\n",
        "    cm = confusion_matrix(y_true, y_pred)\n",
        "\n",
        "    # Calculate percentage accuracy for each element in the confusion matrix\n",
        "    cm_percentage = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] * 100\n",
        "\n",
        "    # Combine counts and percentages for display\n",
        "    annot = np.empty_like(cm).astype(str)\n",
        "    nrows, ncols = cm.shape\n",
        "    for i in range(nrows):\n",
        "        for j in range(ncols):\n",
        "            c = cm[i, j]\n",
        "            p = cm_percentage[i, j]\n",
        "            annot[i, j] = f'{c}\\n({p:.1f}%)'  # Count and percentage\n",
        "\n",
        "    # Plot the confusion matrix with annotations\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    sns.heatmap(cm, annot=annot, fmt='', cmap='Blues', xticklabels=classes, yticklabels=classes, cbar=False)\n",
        "    plt.title(f'Confusion Matrix with Sparsification Threshold {threshold}')\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, 5))\n",
        "    plt.plot(epochs, loss, 'darkblue', label='Training loss')  # Change color to dark blue\n",
        "    if val_loss:\n",
        "        plt.plot(epochs, val_loss, 'lightblue', label='Validation loss')  # Change color to light blue\n",
        "        plt.fill_between(epochs, np.array(val_loss) - np.std(val_loss), np.array(val_loss) + np.std(val_loss), color='lightblue', alpha=0.3)  # Fill between mean - std and mean + std\n",
        "    plt.title(f'Training and validation loss with Sparcification Threshold {threshold}')\n",
        "    # plt.title('Training and Validation Loss on the Original Dataset')\n",
        "\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.ylabel('Loss')\n",
        "    plt.legend()\n",
        "    plt.grid(True)\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(f'Training and validation accuracy with Sparcification Threshold {threshold}')\n",
        "    # plt.title('Training and validation accuracy on the Original Dataset')\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.ylabel('Accuracy')\n",
        "    plt.legend()\n",
        "    plt.grid(True)\n",
        "\n",
        "    plt.show()\n",
        "\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",
        "# 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",
        "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",
        "    # 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",
        "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",
        "    # Feature processing with multiple layers\n",
        "    x_feature = Dense(128, activation='relu', kernel_regularizer=l2(0.01))(x_inp_feature)\n",
        "    x_feature = Dropout(0.5)(x_feature)\n",
        "    x_feature = Dense(64, activation='relu', kernel_regularizer=l2(0.01))(x_feature)\n",
        "    x_feature = Dropout(0.3)(x_feature)\n",
        "\n",
        "    # Adjacency processing with multiple layers\n",
        "    x_adjacency = Dense(128, activation='relu', kernel_regularizer=l2(0.01))(x_inp_adjacency)\n",
        "    x_adjacency = Dropout(0.5)(x_adjacency)\n",
        "    x_adjacency = Dense(64, activation='relu', kernel_regularizer=l2(0.01))(x_adjacency)\n",
        "    x_adjacency = Dropout(0.3)(x_adjacency)\n",
        "\n",
        "    # Concatenate feature and adjacency outputs\n",
        "    x = concatenate([x_feature, x_adjacency])\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=0.0009)\n",
        "\n",
        "    # Compile model\n",
        "    model.compile(optimizer=optimizer, loss=CategoricalCrossentropy(), metrics=['accuracy'])\n",
        "\n",
        "    return model\n",
        "\n",
        "\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",
        "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping\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",
        "        # Early stopping to prevent overfitting\n",
        "        early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n",
        "\n",
        "        # ModelCheckpoint to save the best model\n",
        "        model_checkpoint = ModelCheckpoint('best_gcn_model.keras', monitor='val_loss', save_best_only=True, verbose=1)\n",
        "\n",
        "        start_time = time.time()\n",
        "        # Train the model\n",
        "        history = model.fit([feature_matrix, adjacency_matrix], labels,\n",
        "                            epochs=epochs, batch_size=batch_size,\n",
        "                            validation_split=0.2, callbacks=[early_stopping, model_checkpoint])\n",
        "        end_time = time.time()\n",
        "\n",
        "        # Calculate total training time\n",
        "        total_training_time = end_time - start_time\n",
        "        print(f\"Total training time: {total_training_time:.2f} seconds\")\n",
        "\n",
        "        return history\n",
        "    else:\n",
        "        print(\"Error: One or more input arguments to train_gcn_model is None.\")\n",
        "\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    edges_list_filename = 'sparsified_graph_edges_0.98.csv'\n",
        "    # edges_list_filename ='edges_list_0.8_Full.csv'\n",
        "    summary_data_filename = '1M1L3D_summary.csv'\n",
        "\n",
        "    # Load data\n",
        "    edges_list = pd.read_csv(edges_list_filename, header=None, names=['source', 'target', 'weight'], delimiter=' ')\n",
        "    summary_data = pd.read_csv(summary_data_filename)\n",
        "\n",
        "    node_labels_source = edges_list['source'].astype(str).unique()\n",
        "    node_labels_target = edges_list['target'].astype(str).unique()\n",
        "    node_labels = np.unique(np.concatenate((node_labels_source, node_labels_target)))\n",
        "    node_labels = list(set(node_labels))\n",
        "\n",
        "    print(\"Unique node labels:\", len(node_labels))\n",
        "\n",
        "    summary_data_filtered = summary_data[summary_data['refcode'].isin(node_labels)]\n",
        "    print(\"Filtered summary data:\\n\", len(summary_data_filtered))\n",
        "\n",
        "    if not summary_data_filtered.empty:\n",
        "        linker_smiles = summary_data_filtered['linker SMILES']\n",
        "        if not linker_smiles.empty:\n",
        "            # Generate features\n",
        "\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",
        "\n",
        "            other_features = summary_data_filtered[['Largest Cavity Diameter', 'Largest Free Sphere']].values.astype('float32')\n",
        "            features = np.concatenate((linker_features, metal_features, other_features), axis=1)\n",
        "\n",
        "            # Generate labels\n",
        "            summary_data_filtered['PLD_category'] = pd.cut(\n",
        "                summary_data_filtered['Pore Limiting Diameter'],\n",
        "                bins=[-np.inf, 2.4, 4.4, 5.9, np.inf],\n",
        "                labels=['nonporous', 'small pore', 'medium pore', 'large pore']\n",
        "            )\n",
        "            labels = pd.get_dummies(summary_data_filtered['PLD_category']).values\n",
        "\n",
        "            # Split the data into training and testing sets\n",
        "            X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, random_state=56)\n",
        "\n",
        "            # Load the sparsified graph\n",
        "            graph = nx.read_weighted_edgelist(edges_list_filename)\n",
        "\n",
        "            # Preprocess the graph data\n",
        "            adjacency_matrix, feature_matrix = preprocess_graph(graph, features)\n",
        "\n",
        "            # Split the adjacency and feature matrices accordingly\n",
        "            adj_train, adj_test, feat_train, feat_test = train_test_split(adjacency_matrix, feature_matrix, test_size=0.2, random_state=56)\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(feat_train.shape[1], adj_train.shape[1], num_classes)\n",
        "\n",
        "            # Train the GCN model\n",
        "            history = train_gcn_model(gcn_model, adj_train, feat_train, y_train, epochs=40, batch_size=32)\n",
        "            plot_loss_accuracy(history)\n",
        "            threshold=0.98\n",
        "            # Save the trained GCN model\n",
        "            gcn_model.save(\"gcn_model_0.98.h5\")\n",
        "            # gcn_model.save(\"gcn_model_original.h5\")\n",
        "            # Save the adjacency matrix\n",
        "            np.save(\"adjacency_matrix_0.98.npy\", adjacency_matrix)\n",
        "\n",
        "            # Evaluate the model on the test set\n",
        "            test_loss, test_accuracy = gcn_model.evaluate([feat_test, adj_test], y_test, verbose=0)\n",
        "            print(f'Test Accuracy: {test_accuracy}')\n",
        "\n",
        "\n",
        "            # Load the best model saved by ModelCheckpoint\n",
        "            best_gcn_model = load_model('best_gcn_model.keras')\n",
        "\n",
        "            # Now evaluate the best saved model on the test data\n",
        "            test_loss, test_accuracy = best_gcn_model.evaluate([feat_test, adj_test], y_test, verbose=0)\n",
        "            print(f'Test Accuracy of the best model: {test_accuracy}')\n",
        "\n",
        "            # Generate predictions on the test set\n",
        "            y_pred = np.argmax(gcn_model.predict([feat_test, adj_test]), axis=1)\n",
        "            y_true = np.argmax(y_test, axis=1)\n",
        "\n",
        "            # Define class labels\n",
        "            classes = ['nonporous', 'small pore', 'medium pore', 'large pore']\n",
        "              # Plot loss and accuracy\n",
        "\n",
        "            # Plot confusion matrix for the test set\n",
        "            plot_confusion_matrix(y_true, y_pred, classes)\n",
        "\n",
        "\n",
        "            # Train and evaluate GCN\n",
        "            y_pred_gcn = np.argmax(gcn_model.predict([feat_test, adj_test]), axis=1)\n",
        "            y_true_gcn = np.argmax(y_test, axis=1)\n",
        "            gcn_accuracy = accuracy_score(y_true_gcn, y_pred_gcn)\n",
        "            gcn_kappa = cohen_kappa_score(y_true_gcn, y_pred_gcn)\n",
        "            gcn_mcc = matthews_corrcoef(y_true_gcn, y_pred_gcn)\n",
        "\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",
        "            gbt_classifier = GradientBoostingClassifier(n_estimators=50, learning_rate=0.01, max_depth=2)\n",
        "            gbt_classifier.fit(train_features, np.argmax(train_labels, axis=1))\n",
        "            gbt_pred = gbt_classifier.predict(test_features)\n",
        "            gbt_accuracy = accuracy_score(np.argmax(test_labels, axis=1), gbt_pred)\n",
        "            gbt_kappa = cohen_kappa_score(np.argmax(test_labels, axis=1), gbt_pred)\n",
        "            gbt_mcc = matthews_corrcoef(np.argmax(test_labels, axis=1), gbt_pred)\n",
        "\n",
        "            from sklearn.linear_model import LogisticRegression\n",
        "            from sklearn.metrics import accuracy_score, cohen_kappa_score, matthews_corrcoef\n",
        "\n",
        "            # Train and evaluate Logistic Regression\n",
        "            logreg_classifier = LogisticRegression(max_iter=200)  # You can adjust max_iter if necessary\n",
        "            logreg_classifier.fit(train_features, np.argmax(train_labels, axis=1))  # Train the model\n",
        "\n",
        "            logreg_pred = logreg_classifier.predict(test_features)  # Make predictions on the test set\n",
        "\n",
        "            # Calculate evaluation metrics\n",
        "            logreg_accuracy = accuracy_score(np.argmax(test_labels, axis=1), logreg_pred)\n",
        "            logreg_kappa = cohen_kappa_score(np.argmax(test_labels, axis=1), logreg_pred)\n",
        "            logreg_mcc = matthews_corrcoef(np.argmax(test_labels, axis=1), logreg_pred)\n",
        "\n",
        "\n",
        "            # Train and evaluate Naive Bayes\n",
        "            nb_classifier = GaussianNB()\n",
        "            nb_classifier.fit(train_features, np.argmax(train_labels, axis=1))\n",
        "            nb_pred = nb_classifier.predict(test_features)\n",
        "            nb_accuracy = accuracy_score(np.argmax(test_labels, axis=1), nb_pred)\n",
        "            nb_kappa = cohen_kappa_score(np.argmax(test_labels, axis=1), nb_pred)\n",
        "            nb_mcc = matthews_corrcoef(np.argmax(test_labels, axis=1), nb_pred)\n",
        "\n",
        "\n",
        "            feedforward_model = build_feedforward_model(train_features.shape[1], train_labels.shape[1])\n",
        "            feedforward_model.fit(train_features, train_labels, epochs=20, batch_size=32, validation_split=0.2)\n",
        "            # Evaluate the model on the test set\n",
        "            feedforward_loss, feedforward_accuracy = feedforward_model.evaluate(test_features, test_labels)\n",
        "\n",
        "            # Convert one-hot predictions and labels to class indices\n",
        "            feedforward_predictions = np.argmax(feedforward_model.predict(test_features), axis=1)\n",
        "            true_labels = np.argmax(test_labels, axis=1)\n",
        "\n",
        "            # Calculate Kappa and MCC for DNN\n",
        "            dnn_kappa = cohen_kappa_score(true_labels, feedforward_predictions)\n",
        "            dnn_mcc = matthews_corrcoef(true_labels, feedforward_predictions)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "            # Define your classifiers and evaluation metrics\n",
        "            classifiers = ['GCN', 'GB Trees', 'LR', 'Naive Bayes', 'DNN']\n",
        "            accuracies = [gcn_accuracy, gbt_accuracy, logreg_accuracy, nb_accuracy, feedforward_accuracy]\n",
        "            kappa_scores = [gcn_kappa, gbt_kappa, logreg_kappa, nb_kappa, dnn_kappa]\n",
        "            mcc_scores = [gcn_mcc, gbt_mcc, logreg_mcc, nb_mcc, dnn_mcc]\n",
        "\n",
        "            # Print evaluation metrics\n",
        "            print(\"Classifier Evaluation Metrics:\")\n",
        "            for clf, acc, kappa, mcc in zip(classifiers, accuracies, kappa_scores, mcc_scores):\n",
        "                print(f\"{clf}: Accuracy={acc:.3f}, Kappa={kappa:.3f}, MCC={mcc:.3f}\")\n",
        "\n",
        "            # Plot all metrics in the same style\n",
        "            colors = plt.cm.Blues(np.linspace(0.9, 0.3, len(classifiers)))\n",
        "\n",
        "            # Plot accuracy\n",
        "            plt.figure(figsize=(8, 5))\n",
        "            plt.bar(classifiers, accuracies, color=colors)\n",
        "            plt.title('Accuracy for Classifiers')\n",
        "            plt.xlabel('Classifier')\n",
        "            plt.ylabel('Accuracy')\n",
        "            plt.ylim(0, 1)  # Start from 0, no negative values\n",
        "            plt.grid(True, which=\"both\", ls=\"--\")\n",
        "            plt.tight_layout()\n",
        "            plt.show()\n",
        "\n",
        "            # Plot kappa scores\n",
        "            plt.figure(figsize=(8, 5))\n",
        "            plt.bar(classifiers, kappa_scores, color=colors)\n",
        "            plt.title('Kappa Scores for Classifiers')\n",
        "            plt.xlabel('Classifier')\n",
        "            plt.ylabel('Kappa')\n",
        "            plt.ylim(0, 1)  # Start from 0\n",
        "            plt.grid(True, which=\"both\", ls=\"--\")\n",
        "            plt.tight_layout()\n",
        "            plt.show()\n",
        "\n",
        "            # Plot MCC scores\n",
        "            plt.figure(figsize=(8, 5))\n",
        "            plt.bar(classifiers, mcc_scores, color=colors)\n",
        "            plt.title('MCC Scores for Classifiers')\n",
        "            plt.xlabel('Classifier')\n",
        "            plt.ylabel('MCC')\n",
        "            plt.ylim(0, 1)  # Start from 0\n",
        "            plt.grid(True, which=\"both\", ls=\"--\")\n",
        "            plt.tight_layout()\n",
        "            plt.show()\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "        else:\n",
        "            print(\"Error: linker_smiles column is empty.\")\n",
        "    else:\n",
        "        print(\"Error: summary_data_filtered DataFrame is empty.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Kt5XvW3hxB8q",
        "outputId": "72b4c22c-ed04-456c-8123-db431a417529"
      },
      "execution_count": 3,
      "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-3-bdd4d2434d75>:279: 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(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/40\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - accuracy: 0.3163 - loss: 6.0858\n",
            "Epoch 1: val_loss improved from inf to 2.74295, saving model to best_gcn_model.keras\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 103ms/step - accuracy: 0.3176 - loss: 6.0564 - val_accuracy: 0.6361 - val_loss: 2.7429\n",
            "Epoch 2/40\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - accuracy: 0.4970 - loss: 2.5097\n",
            "Epoch 2: val_loss improved from 2.74295 to 1.72149, saving model to best_gcn_model.keras\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 56ms/step - accuracy: 0.4971 - loss: 2.5045 - val_accuracy: 0.6982 - val_loss: 1.7215\n",
            "Epoch 3/40\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - accuracy: 0.6106 - loss: 1.7177\n",
            "Epoch 3: val_loss improved from 1.72149 to 1.33838, saving model to best_gcn_model.keras\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 67ms/step - accuracy: 0.6104 - loss: 1.7162 - val_accuracy: 0.7574 - val_loss: 1.3384\n",
            "Epoch 4/40\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - accuracy: 0.5982 - loss: 1.4504\n",
            "Epoch 4: val_loss improved from 1.33838 to 1.13247, saving model to best_gcn_model.keras\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 58ms/step - accuracy: 0.5986 - loss: 1.4492 - val_accuracy: 0.7485 - val_loss: 1.1325\n",
            "Epoch 5/40\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 0.6386 - loss: 1.2515\n",
            "Epoch 5: val_loss improved from 1.13247 to 1.02794, saving model to best_gcn_model.keras\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 25ms/step - accuracy: 0.6387 - loss: 1.2509 - val_accuracy: 0.7899 - val_loss: 1.0279\n",
            "Epoch 6/40\n",
            "\u001b[1m42/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.6423 - loss: 1.1556\n",
            "Epoch 6: val_loss improved from 1.02794 to 0.93280, saving model to best_gcn_model.keras\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - accuracy: 0.6421 - loss: 1.1553 - val_accuracy: 0.7633 - val_loss: 0.9328\n",
            "Epoch 7/40\n",
            "\u001b[1m38/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.6752 - loss: 1.0724\n",
            "Epoch 7: val_loss improved from 0.93280 to 0.86721, saving model to best_gcn_model.keras\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - accuracy: 0.6755 - loss: 1.0710 - val_accuracy: 0.7781 - val_loss: 0.8672\n",
            "Epoch 8/40\n",
            "\u001b[1m39/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.6584 - loss: 1.0586\n",
            "Epoch 8: val_loss improved from 0.86721 to 0.84938, saving model to best_gcn_model.keras\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - accuracy: 0.6606 - loss: 1.0557 - val_accuracy: 0.7604 - val_loss: 0.8494\n",
            "Epoch 9/40\n",
            "\u001b[1m42/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.6619 - loss: 1.0154\n",
            "Epoch 9: val_loss improved from 0.84938 to 0.82937, saving model to best_gcn_model.keras\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 12ms/step - accuracy: 0.6616 - loss: 1.0152 - val_accuracy: 0.7367 - val_loss: 0.8294\n",
            "Epoch 10/40\n",
            "\u001b[1m40/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.6845 - loss: 0.9545\n",
            "Epoch 10: val_loss improved from 0.82937 to 0.80190, saving model to best_gcn_model.keras\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - accuracy: 0.6848 - loss: 0.9555 - val_accuracy: 0.7663 - val_loss: 0.8019\n",
            "Epoch 11/40\n",
            "\u001b[1m41/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.6824 - loss: 0.9390\n",
            "Epoch 11: val_loss improved from 0.80190 to 0.78866, saving model to best_gcn_model.keras\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - accuracy: 0.6837 - loss: 0.9386 - val_accuracy: 0.7781 - val_loss: 0.7887\n",
            "Epoch 12/40\n",
            "\u001b[1m38/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.7052 - loss: 0.9190\n",
            "Epoch 12: val_loss improved from 0.78866 to 0.75190, saving model to best_gcn_model.keras\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - accuracy: 0.7048 - loss: 0.9183 - val_accuracy: 0.7840 - val_loss: 0.7519\n",
            "Epoch 13/40\n",
            "\u001b[1m40/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.6992 - loss: 0.8779\n",
            "Epoch 13: val_loss improved from 0.75190 to 0.74438, saving model to best_gcn_model.keras\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - accuracy: 0.6981 - loss: 0.8800 - val_accuracy: 0.7751 - val_loss: 0.7444\n",
            "Epoch 14/40\n",
            "\u001b[1m38/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.7134 - loss: 0.8688\n",
            "Epoch 14: val_loss improved from 0.74438 to 0.74251, saving model to best_gcn_model.keras\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - accuracy: 0.7121 - loss: 0.8712 - val_accuracy: 0.7751 - val_loss: 0.7425\n",
            "Epoch 15/40\n",
            "\u001b[1m42/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.6651 - loss: 0.8715\n",
            "Epoch 15: val_loss improved from 0.74251 to 0.72120, saving model to best_gcn_model.keras\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - accuracy: 0.6652 - loss: 0.8719 - val_accuracy: 0.7959 - val_loss: 0.7212\n",
            "Epoch 16/40\n",
            "\u001b[1m40/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.7086 - loss: 0.8352\n",
            "Epoch 16: val_loss improved from 0.72120 to 0.70048, saving model to best_gcn_model.keras\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 18ms/step - accuracy: 0.7078 - loss: 0.8364 - val_accuracy: 0.7870 - val_loss: 0.7005\n",
            "Epoch 17/40\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.7054 - loss: 0.8330\n",
            "Epoch 17: val_loss improved from 0.70048 to 0.69342, saving model to best_gcn_model.keras\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 20ms/step - accuracy: 0.7056 - loss: 0.8329 - val_accuracy: 0.8018 - val_loss: 0.6934\n",
            "Epoch 18/40\n",
            "\u001b[1m41/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - accuracy: 0.7031 - loss: 0.8263\n",
            "Epoch 18: val_loss did not improve from 0.69342\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - accuracy: 0.7031 - loss: 0.8268 - val_accuracy: 0.7781 - val_loss: 0.7023\n",
            "Epoch 19/40\n",
            "\u001b[1m41/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - accuracy: 0.6925 - loss: 0.8480\n",
            "Epoch 19: val_loss improved from 0.69342 to 0.67872, saving model to best_gcn_model.keras\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 22ms/step - accuracy: 0.6938 - loss: 0.8475 - val_accuracy: 0.7899 - val_loss: 0.6787\n",
            "Epoch 20/40\n",
            "\u001b[1m41/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.7213 - loss: 0.8201\n",
            "Epoch 20: val_loss did not improve from 0.67872\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - accuracy: 0.7212 - loss: 0.8202 - val_accuracy: 0.7633 - val_loss: 0.6836\n",
            "Epoch 21/40\n",
            "\u001b[1m38/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.7308 - loss: 0.8071\n",
            "Epoch 21: val_loss improved from 0.67872 to 0.66496, saving model to best_gcn_model.keras\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - accuracy: 0.7302 - loss: 0.8056 - val_accuracy: 0.7811 - val_loss: 0.6650\n",
            "Epoch 22/40\n",
            "\u001b[1m37/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.7086 - loss: 0.8163\n",
            "Epoch 22: val_loss did not improve from 0.66496\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.7087 - loss: 0.8153 - val_accuracy: 0.7840 - val_loss: 0.6688\n",
            "Epoch 23/40\n",
            "\u001b[1m41/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.7071 - loss: 0.8116\n",
            "Epoch 23: val_loss improved from 0.66496 to 0.64427, saving model to best_gcn_model.keras\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - accuracy: 0.7066 - loss: 0.8115 - val_accuracy: 0.8077 - val_loss: 0.6443\n",
            "Epoch 24/40\n",
            "\u001b[1m38/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.7452 - loss: 0.7915\n",
            "Epoch 24: val_loss did not improve from 0.64427\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.7425 - loss: 0.7919 - val_accuracy: 0.7899 - val_loss: 0.6606\n",
            "Epoch 25/40\n",
            "\u001b[1m41/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.7312 - loss: 0.7852\n",
            "Epoch 25: val_loss improved from 0.64427 to 0.64005, saving model to best_gcn_model.keras\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 14ms/step - accuracy: 0.7305 - loss: 0.7855 - val_accuracy: 0.8047 - val_loss: 0.6401\n",
            "Epoch 26/40\n",
            "\u001b[1m38/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.7003 - loss: 0.8155\n",
            "Epoch 26: val_loss did not improve from 0.64005\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - accuracy: 0.7019 - loss: 0.8132 - val_accuracy: 0.8047 - val_loss: 0.6414\n",
            "Epoch 27/40\n",
            "\u001b[1m39/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.7131 - loss: 0.7794\n",
            "Epoch 27: val_loss improved from 0.64005 to 0.63767, saving model to best_gcn_model.keras\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - accuracy: 0.7111 - loss: 0.7829 - val_accuracy: 0.8107 - val_loss: 0.6377\n",
            "Epoch 28/40\n",
            "\u001b[1m39/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.7033 - loss: 0.7708\n",
            "Epoch 28: val_loss improved from 0.63767 to 0.62342, saving model to best_gcn_model.keras\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - accuracy: 0.7047 - loss: 0.7706 - val_accuracy: 0.8077 - val_loss: 0.6234\n",
            "Epoch 29/40\n",
            "\u001b[1m42/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.7233 - loss: 0.7874\n",
            "Epoch 29: val_loss did not improve from 0.62342\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.7230 - loss: 0.7869 - val_accuracy: 0.8195 - val_loss: 0.6306\n",
            "Epoch 30/40\n",
            "\u001b[1m37/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.6943 - loss: 0.8303\n",
            "Epoch 30: val_loss improved from 0.62342 to 0.61745, saving model to best_gcn_model.keras\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - accuracy: 0.6980 - loss: 0.8240 - val_accuracy: 0.7988 - val_loss: 0.6174\n",
            "Epoch 31/40\n",
            "\u001b[1m40/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.7198 - loss: 0.7656\n",
            "Epoch 31: val_loss did not improve from 0.61745\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.7202 - loss: 0.7673 - val_accuracy: 0.8107 - val_loss: 0.6370\n",
            "Epoch 32/40\n",
            "\u001b[1m38/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - accuracy: 0.7563 - loss: 0.7336\n",
            "Epoch 32: val_loss did not improve from 0.61745\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - accuracy: 0.7534 - loss: 0.7367 - val_accuracy: 0.8254 - val_loss: 0.6224\n",
            "Epoch 33/40\n",
            "\u001b[1m39/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.7435 - loss: 0.7361\n",
            "Epoch 33: val_loss did not improve from 0.61745\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.7438 - loss: 0.7372 - val_accuracy: 0.8077 - val_loss: 0.6205\n",
            "Epoch 34/40\n",
            "\u001b[1m40/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 0.7405 - loss: 0.7371\n",
            "Epoch 34: val_loss did not improve from 0.61745\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - accuracy: 0.7396 - loss: 0.7388 - val_accuracy: 0.7899 - val_loss: 0.6384\n",
            "Epoch 35/40\n",
            "\u001b[1m42/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 16ms/step - accuracy: 0.7291 - loss: 0.7503\n",
            "Epoch 35: val_loss did not improve from 0.61745\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step - accuracy: 0.7288 - loss: 0.7510 - val_accuracy: 0.7959 - val_loss: 0.6256\n",
            "Total training time: 47.43 seconds\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy: 0.7517730593681335\n",
            "Test Accuracy of the best model: 0.7517730593681335\n",
            "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 18ms/step - accuracy: 0.2735 - loss: 2.7791 - val_accuracy: 0.5414 - val_loss: 1.7142\n",
            "Epoch 2/20\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - accuracy: 0.4699 - loss: 1.6781 - val_accuracy: 0.5888 - val_loss: 1.3693\n",
            "Epoch 3/20\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.5907 - loss: 1.3548 - val_accuracy: 0.6627 - val_loss: 1.1787\n",
            "Epoch 4/20\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.6429 - loss: 1.1702 - val_accuracy: 0.6568 - val_loss: 1.0862\n",
            "Epoch 5/20\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6454 - loss: 1.1070 - val_accuracy: 0.6805 - val_loss: 0.9944\n",
            "Epoch 6/20\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6879 - loss: 1.0008 - val_accuracy: 0.7485 - val_loss: 0.9521\n",
            "Epoch 7/20\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6695 - loss: 0.9736 - val_accuracy: 0.7456 - val_loss: 0.9052\n",
            "Epoch 8/20\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6963 - loss: 0.9360 - val_accuracy: 0.6864 - val_loss: 0.9264\n",
            "Epoch 9/20\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7123 - loss: 0.9161 - val_accuracy: 0.7604 - val_loss: 0.8469\n",
            "Epoch 10/20\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6995 - loss: 0.9096 - val_accuracy: 0.7130 - val_loss: 0.8429\n",
            "Epoch 11/20\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7368 - loss: 0.8422 - val_accuracy: 0.7515 - val_loss: 0.8123\n",
            "Epoch 12/20\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6837 - loss: 0.8896 - val_accuracy: 0.7278 - val_loss: 0.8219\n",
            "Epoch 13/20\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7109 - loss: 0.8307 - val_accuracy: 0.7574 - val_loss: 0.7889\n",
            "Epoch 14/20\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.6978 - loss: 0.8380 - val_accuracy: 0.7633 - val_loss: 0.8054\n",
            "Epoch 15/20\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.7143 - loss: 0.8026 - val_accuracy: 0.7781 - val_loss: 0.7793\n",
            "Epoch 16/20\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.7187 - loss: 0.7983 - val_accuracy: 0.7692 - val_loss: 0.7749\n",
            "Epoch 17/20\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.7213 - loss: 0.8012 - val_accuracy: 0.7633 - val_loss: 0.7590\n",
            "Epoch 18/20\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.7261 - loss: 0.7955 - val_accuracy: 0.7367 - val_loss: 0.7682\n",
            "Epoch 19/20\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.7219 - loss: 0.7910 - val_accuracy: 0.7515 - val_loss: 0.7416\n",
            "Epoch 20/20\n",
            "\u001b[1m43/43\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.7193 - loss: 0.7775 - val_accuracy: 0.7929 - val_loss: 0.7538\n",
            "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.7368 - loss: 0.8203 \n",
            "\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step\n",
            "Classifier Evaluation Metrics:\n",
            "GCN: Accuracy=0.752, Kappa=0.641, MCC=0.647\n",
            "GB Trees: Accuracy=0.655, Kappa=0.509, MCC=0.525\n",
            "LR: Accuracy=0.714, Kappa=0.596, MCC=0.598\n",
            "Naive Bayes: Accuracy=0.463, Kappa=0.290, MCC=0.309\n",
            "DNN: Accuracy=0.742, Kappa=0.637, MCC=0.639\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}