CompareMLMethods_Sparcified_0_9.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOhgLcW8tOCvkSKM5apVfIa",
      "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_9.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": "_Se3urvBfuK1",
        "outputId": "25090a9c-daf8-4c0c-9502-47cf43a30749"
      },
      "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": "gZyYMwkNfzWq",
        "outputId": "7ae6dcce-7e86-4e81-cc37-7792d912aeff"
      },
      "execution_count": 3,
      "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[31m23.8 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=0.90\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",
        "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",
        "        # Train the model\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_0.9.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=0.9\n",
        "            # Save the trained GCN model\n",
        "            gcn_model.save(\"gcn_model_0.9.h5\")\n",
        "            # gcn_model.save(\"gcn_model_original.h5\")\n",
        "            # Save the adjacency matrix\n",
        "            np.save(\"adjacency_matrix_0.9.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=500)  # 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",
        "            # 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",
        "\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": "4PpjPJAEf1vB",
        "outputId": "159d939a-8b3e-4702-e1f8-4b7a6cb29804"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unique node labels: 9030\n",
            "Filtered summary data:\n",
            " 9030\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-4-02521b5aeb22>:278: 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[1m179/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - accuracy: 0.4362 - loss: 3.8957\n",
            "Epoch 1: val_loss improved from inf to 1.27312, saving model to best_gcn_model.keras\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 34ms/step - accuracy: 0.4374 - loss: 3.8727 - val_accuracy: 0.6734 - val_loss: 1.2731\n",
            "Epoch 2/40\n",
            "\u001b[1m179/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - accuracy: 0.6288 - loss: 1.2463\n",
            "Epoch 2: val_loss improved from 1.27312 to 1.03754, saving model to best_gcn_model.keras\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 29ms/step - accuracy: 0.6289 - loss: 1.2452 - val_accuracy: 0.7045 - val_loss: 1.0375\n",
            "Epoch 3/40\n",
            "\u001b[1m180/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - accuracy: 0.6495 - loss: 1.0390\n",
            "Epoch 3: val_loss improved from 1.03754 to 0.91966, saving model to best_gcn_model.keras\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 21ms/step - accuracy: 0.6496 - loss: 1.0388 - val_accuracy: 0.6678 - val_loss: 0.9197\n",
            "Epoch 4/40\n",
            "\u001b[1m179/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - accuracy: 0.6756 - loss: 0.9345\n",
            "Epoch 4: val_loss improved from 0.91966 to 0.83914, saving model to best_gcn_model.keras\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 28ms/step - accuracy: 0.6756 - loss: 0.9342 - val_accuracy: 0.6955 - val_loss: 0.8391\n",
            "Epoch 5/40\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - accuracy: 0.6712 - loss: 0.8864\n",
            "Epoch 5: val_loss improved from 0.83914 to 0.80418, saving model to best_gcn_model.keras\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 22ms/step - accuracy: 0.6712 - loss: 0.8863 - val_accuracy: 0.7003 - val_loss: 0.8042\n",
            "Epoch 6/40\n",
            "\u001b[1m180/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 0.6871 - loss: 0.8275\n",
            "Epoch 6: val_loss improved from 0.80418 to 0.78558, saving model to best_gcn_model.keras\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 28ms/step - accuracy: 0.6870 - loss: 0.8275 - val_accuracy: 0.7024 - val_loss: 0.7856\n",
            "Epoch 7/40\n",
            "\u001b[1m179/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - accuracy: 0.7043 - loss: 0.7925\n",
            "Epoch 7: val_loss improved from 0.78558 to 0.76812, saving model to best_gcn_model.keras\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.7040 - loss: 0.7928 - val_accuracy: 0.7128 - val_loss: 0.7681\n",
            "Epoch 8/40\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - accuracy: 0.6863 - loss: 0.8045\n",
            "Epoch 8: val_loss did not improve from 0.76812\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.6863 - loss: 0.8044 - val_accuracy: 0.6990 - val_loss: 0.7819\n",
            "Epoch 9/40\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - accuracy: 0.6957 - loss: 0.7859\n",
            "Epoch 9: val_loss improved from 0.76812 to 0.75421, saving model to best_gcn_model.keras\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 32ms/step - accuracy: 0.6958 - loss: 0.7859 - val_accuracy: 0.7080 - val_loss: 0.7542\n",
            "Epoch 10/40\n",
            "\u001b[1m180/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - accuracy: 0.6993 - loss: 0.7769\n",
            "Epoch 10: val_loss improved from 0.75421 to 0.73144, saving model to best_gcn_model.keras\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 21ms/step - accuracy: 0.6993 - loss: 0.7770 - val_accuracy: 0.7280 - val_loss: 0.7314\n",
            "Epoch 11/40\n",
            "\u001b[1m180/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - accuracy: 0.7108 - loss: 0.7493\n",
            "Epoch 11: val_loss did not improve from 0.73144\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 31ms/step - accuracy: 0.7107 - loss: 0.7495 - val_accuracy: 0.6997 - val_loss: 0.7650\n",
            "Epoch 12/40\n",
            "\u001b[1m178/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - accuracy: 0.7110 - loss: 0.7514\n",
            "Epoch 12: val_loss improved from 0.73144 to 0.73027, saving model to best_gcn_model.keras\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.7109 - loss: 0.7515 - val_accuracy: 0.7273 - val_loss: 0.7303\n",
            "Epoch 13/40\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.6975 - loss: 0.7668\n",
            "Epoch 13: val_loss improved from 0.73027 to 0.72593, saving model to best_gcn_model.keras\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.6975 - loss: 0.7668 - val_accuracy: 0.7156 - val_loss: 0.7259\n",
            "Epoch 14/40\n",
            "\u001b[1m178/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - accuracy: 0.7145 - loss: 0.7514\n",
            "Epoch 14: val_loss did not improve from 0.72593\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 36ms/step - accuracy: 0.7144 - loss: 0.7515 - val_accuracy: 0.6886 - val_loss: 0.7546\n",
            "Epoch 15/40\n",
            "\u001b[1m180/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - accuracy: 0.7094 - loss: 0.7540\n",
            "Epoch 15: val_loss improved from 0.72593 to 0.71788, saving model to best_gcn_model.keras\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 37ms/step - accuracy: 0.7094 - loss: 0.7539 - val_accuracy: 0.7370 - val_loss: 0.7179\n",
            "Epoch 16/40\n",
            "\u001b[1m178/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - accuracy: 0.7061 - loss: 0.7439\n",
            "Epoch 16: val_loss did not improve from 0.71788\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 20ms/step - accuracy: 0.7063 - loss: 0.7437 - val_accuracy: 0.7253 - val_loss: 0.7266\n",
            "Epoch 17/40\n",
            "\u001b[1m180/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - accuracy: 0.7320 - loss: 0.7092\n",
            "Epoch 17: val_loss improved from 0.71788 to 0.70621, saving model to best_gcn_model.keras\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7318 - loss: 0.7095 - val_accuracy: 0.7260 - val_loss: 0.7062\n",
            "Epoch 18/40\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - accuracy: 0.7186 - loss: 0.7336\n",
            "Epoch 18: val_loss did not improve from 0.70621\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 32ms/step - accuracy: 0.7186 - loss: 0.7336 - val_accuracy: 0.7253 - val_loss: 0.7199\n",
            "Epoch 19/40\n",
            "\u001b[1m179/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - accuracy: 0.7132 - loss: 0.7265\n",
            "Epoch 19: val_loss did not improve from 0.70621\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 19ms/step - accuracy: 0.7132 - loss: 0.7266 - val_accuracy: 0.7343 - val_loss: 0.7070\n",
            "Epoch 20/40\n",
            "\u001b[1m180/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.7163 - loss: 0.7252\n",
            "Epoch 20: val_loss did not improve from 0.70621\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 25ms/step - accuracy: 0.7163 - loss: 0.7252 - val_accuracy: 0.7253 - val_loss: 0.7072\n",
            "Epoch 21/40\n",
            "\u001b[1m179/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 0.7268 - loss: 0.7082\n",
            "Epoch 21: val_loss improved from 0.70621 to 0.70310, saving model to best_gcn_model.keras\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 27ms/step - accuracy: 0.7267 - loss: 0.7084 - val_accuracy: 0.7273 - val_loss: 0.7031\n",
            "Epoch 22/40\n",
            "\u001b[1m180/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - accuracy: 0.7370 - loss: 0.7093\n",
            "Epoch 22: val_loss improved from 0.70310 to 0.69747, saving model to best_gcn_model.keras\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.7369 - loss: 0.7095 - val_accuracy: 0.7308 - val_loss: 0.6975\n",
            "Epoch 23/40\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step - accuracy: 0.7275 - loss: 0.7095\n",
            "Epoch 23: val_loss did not improve from 0.69747\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7274 - loss: 0.7096 - val_accuracy: 0.7356 - val_loss: 0.7101\n",
            "Epoch 24/40\n",
            "\u001b[1m179/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 0.7258 - loss: 0.7044\n",
            "Epoch 24: val_loss improved from 0.69747 to 0.69703, saving model to best_gcn_model.keras\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 27ms/step - accuracy: 0.7258 - loss: 0.7046 - val_accuracy: 0.7405 - val_loss: 0.6970\n",
            "Epoch 25/40\n",
            "\u001b[1m179/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - accuracy: 0.7283 - loss: 0.7195\n",
            "Epoch 25: val_loss did not improve from 0.69703\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.7281 - loss: 0.7195 - val_accuracy: 0.7349 - val_loss: 0.7142\n",
            "Epoch 26/40\n",
            "\u001b[1m179/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 18ms/step - accuracy: 0.7301 - loss: 0.7217\n",
            "Epoch 26: val_loss did not improve from 0.69703\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.7301 - loss: 0.7217 - val_accuracy: 0.7384 - val_loss: 0.7059\n",
            "Epoch 27/40\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - accuracy: 0.7234 - loss: 0.7169\n",
            "Epoch 27: val_loss improved from 0.69703 to 0.69213, saving model to best_gcn_model.keras\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 30ms/step - accuracy: 0.7233 - loss: 0.7169 - val_accuracy: 0.7460 - val_loss: 0.6921\n",
            "Epoch 28/40\n",
            "\u001b[1m178/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - accuracy: 0.7436 - loss: 0.6971\n",
            "Epoch 28: val_loss did not improve from 0.69213\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 20ms/step - accuracy: 0.7435 - loss: 0.6973 - val_accuracy: 0.7363 - val_loss: 0.6963\n",
            "Epoch 29/40\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - accuracy: 0.7330 - loss: 0.6963\n",
            "Epoch 29: val_loss did not improve from 0.69213\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 31ms/step - accuracy: 0.7330 - loss: 0.6963 - val_accuracy: 0.7343 - val_loss: 0.6972\n",
            "Epoch 30/40\n",
            "\u001b[1m180/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - accuracy: 0.7327 - loss: 0.6974\n",
            "Epoch 30: val_loss improved from 0.69213 to 0.68689, saving model to best_gcn_model.keras\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 22ms/step - accuracy: 0.7326 - loss: 0.6976 - val_accuracy: 0.7536 - val_loss: 0.6869\n",
            "Epoch 31/40\n",
            "\u001b[1m180/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.7302 - loss: 0.6982\n",
            "Epoch 31: val_loss improved from 0.68689 to 0.68634, saving model to best_gcn_model.keras\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 26ms/step - accuracy: 0.7301 - loss: 0.6983 - val_accuracy: 0.7391 - val_loss: 0.6863\n",
            "Epoch 32/40\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - accuracy: 0.7285 - loss: 0.7132\n",
            "Epoch 32: val_loss improved from 0.68634 to 0.68616, saving model to best_gcn_model.keras\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 32ms/step - accuracy: 0.7285 - loss: 0.7131 - val_accuracy: 0.7391 - val_loss: 0.6862\n",
            "Epoch 33/40\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - accuracy: 0.7266 - loss: 0.6987\n",
            "Epoch 33: val_loss did not improve from 0.68616\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 20ms/step - accuracy: 0.7266 - loss: 0.6988 - val_accuracy: 0.7308 - val_loss: 0.7045\n",
            "Epoch 34/40\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - accuracy: 0.7326 - loss: 0.7085\n",
            "Epoch 34: val_loss did not improve from 0.68616\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 30ms/step - accuracy: 0.7326 - loss: 0.7085 - val_accuracy: 0.7343 - val_loss: 0.7068\n",
            "Epoch 35/40\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - accuracy: 0.7274 - loss: 0.7106\n",
            "Epoch 35: val_loss did not improve from 0.68616\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 21ms/step - accuracy: 0.7273 - loss: 0.7106 - val_accuracy: 0.7481 - val_loss: 0.6880\n",
            "Epoch 36/40\n",
            "\u001b[1m180/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 0.7310 - loss: 0.7014\n",
            "Epoch 36: val_loss did not improve from 0.68616\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 28ms/step - accuracy: 0.7310 - loss: 0.7014 - val_accuracy: 0.7349 - val_loss: 0.6862\n",
            "Epoch 37/40\n",
            "\u001b[1m180/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - accuracy: 0.7313 - loss: 0.6958\n",
            "Epoch 37: val_loss did not improve from 0.68616\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 21ms/step - accuracy: 0.7314 - loss: 0.6959 - val_accuracy: 0.7287 - val_loss: 0.6868\n",
            "Total training time: 235.82 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: 0.7264673113822937\n",
            "Test Accuracy of the best model: 0.7264673113822937\n",
            "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/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[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/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[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - accuracy: 0.3992 - loss: 2.2259 - val_accuracy: 0.6519 - val_loss: 1.1261\n",
            "Epoch 2/20\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.6185 - loss: 1.1098 - val_accuracy: 0.6844 - val_loss: 0.9137\n",
            "Epoch 3/20\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.6687 - loss: 0.9561 - val_accuracy: 0.7024 - val_loss: 0.8523\n",
            "Epoch 4/20\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.6870 - loss: 0.8831 - val_accuracy: 0.7190 - val_loss: 0.8037\n",
            "Epoch 5/20\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.6998 - loss: 0.8407 - val_accuracy: 0.7176 - val_loss: 0.7863\n",
            "Epoch 6/20\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.6971 - loss: 0.8139 - val_accuracy: 0.7218 - val_loss: 0.7653\n",
            "Epoch 7/20\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7002 - loss: 0.8073 - val_accuracy: 0.7142 - val_loss: 0.7464\n",
            "Epoch 8/20\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7055 - loss: 0.7826 - val_accuracy: 0.7426 - val_loss: 0.7457\n",
            "Epoch 9/20\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7032 - loss: 0.7658 - val_accuracy: 0.7522 - val_loss: 0.7278\n",
            "Epoch 10/20\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7163 - loss: 0.7588 - val_accuracy: 0.7287 - val_loss: 0.7227\n",
            "Epoch 11/20\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7129 - loss: 0.7545 - val_accuracy: 0.7439 - val_loss: 0.7227\n",
            "Epoch 12/20\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7089 - loss: 0.7498 - val_accuracy: 0.7197 - val_loss: 0.7169\n",
            "Epoch 13/20\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7119 - loss: 0.7490 - val_accuracy: 0.7336 - val_loss: 0.7414\n",
            "Epoch 14/20\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7141 - loss: 0.7391 - val_accuracy: 0.7446 - val_loss: 0.7136\n",
            "Epoch 15/20\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.7228 - loss: 0.7203 - val_accuracy: 0.7253 - val_loss: 0.6967\n",
            "Epoch 16/20\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7320 - loss: 0.7053 - val_accuracy: 0.7336 - val_loss: 0.6955\n",
            "Epoch 17/20\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.7180 - loss: 0.7251 - val_accuracy: 0.7329 - val_loss: 0.7033\n",
            "Epoch 18/20\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.7220 - loss: 0.7188 - val_accuracy: 0.7467 - val_loss: 0.7140\n",
            "Epoch 19/20\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.7249 - loss: 0.7166 - val_accuracy: 0.7370 - val_loss: 0.6904\n",
            "Epoch 20/20\n",
            "\u001b[1m181/181\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.7296 - loss: 0.7067 - val_accuracy: 0.7502 - val_loss: 0.6896\n",
            "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.7229 - loss: 0.7024\n",
            "\u001b[1m57/57\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step\n",
            "Classifier Evaluation Metrics:\n",
            "GCN: Accuracy=0.726, Kappa=0.610, MCC=0.613\n",
            "GB Trees: Accuracy=0.688, Kappa=0.548, MCC=0.560\n",
            "LR: Accuracy=0.741, Kappa=0.625, MCC=0.627\n",
            "Naive Bayes: Accuracy=0.332, Kappa=0.171, MCC=0.203\n",
            "DNN: Accuracy=0.733, Kappa=0.616, MCC=0.619\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": {}
        }
      ]
    }
  ]
}