FL_Embedding_Classifier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HvqlPD_IG-ea",
    "outputId": "d09a3a5e-43d1-42bb-de1a-43ad6ed0bb88"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "# Install the libraries\n",
    "!pip install sentence_transformers\n",
    "!pip install pandas\n",
    "!pip install numpy\n",
    "!pip install scikit-learn\n",
    "!pip install matplotlib\n",
    "!pip install seaborn\n",
    "!pip install tf-keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1fxIGnXgHY1_",
    "outputId": "2eb2b7cb-f661-43f4-f525-6f2ee7f54d31"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\tf_keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Import the packages\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import os\n",
    "import json\n",
    "from sklearn.manifold import TSNE\n",
    "import numpy as np\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, roc_auc_score, confusion_matrix, recall_score\n",
    "from sklearn.model_selection import cross_validate\n",
    "from sentence_transformers import SentenceTransformer\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Dropout\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.losses import BinaryCrossentropy\n",
    "from tensorflow.keras.metrics import AUC\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n"
   ]
  },
  {
   "cell_type": "code",
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      "a0fd105ea5f046c18b8191d1ae5d89bc",
      "2319ac540cdb44eba7715740e1f391b3"
     ]
    },
    "id": "9KitraTVHf1d",
    "outputId": "448a6dff-f056-4f2b-9b40-b1ea1e3bf8b3"
   },
   "outputs": [],
   "source": [
    "# Load the embedding model\n",
    "model = SentenceTransformer(\"all-MiniLM-L6-v2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "qaurbTtCGu0q"
   },
   "outputs": [],
   "source": [
    "# Load the datasets and create embeddings\n",
    "file_path = r\"data\\IU-GroundTruth.csv\"\n",
    "report_column = 'REPORT'\n",
    "df = pd.read_csv(file_path)\n",
    "allReports = df[report_column]\n",
    "allEmbeddings = [];\n",
    "\n",
    "for report in allReports:\n",
    "    response = model.encode(report)\n",
    "    allEmbeddings.append(response)\n",
    "\n",
    "label_columns = ['Enlarged Cardiom.','Cardiomegaly','Lung Lesion','Lung Opacity','Edema','Consolidation','Pneumonia','Atelectasis','Pneumothorax','Pleural Effusion','Pleural Other','Fracture','Support Devices']\n",
    "labels = df[label_columns].values\n",
    "\n",
    "allEmbeddings = np.array(allEmbeddings)\n",
    "labels = np.array(labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "78g-W3kJsAx-"
   },
   "source": [
    "# Federated Learning with Surgical Aggregation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "qNfkcRaZYDEf"
   },
   "outputs": [],
   "source": [
    "# Function to create an MLP classifier\n",
    "def create_mlp_model(input_dim, output_dim):\n",
    "    model = Sequential()\n",
    "\n",
    "    inputs = keras.Input(shape=(input_dim,))\n",
    "    x = layers.Dense(512, activation=\"relu\")(inputs)\n",
    "    x = layers.Dropout(0.2)(x) # optional dropout\n",
    "    x = layers.Dense(256, activation=\"relu\")(x)\n",
    "    x = layers.Dropout(0.2)(x) # optional dropout\n",
    "    outputs = layers.Dense(output_dim, activation=\"sigmoid\")(x)\n",
    "    model = keras.Model(inputs=inputs, outputs=outputs)\n",
    "\n",
    "\n",
    "\n",
    "    # Compile model\n",
    "    model.compile(optimizer=Adam(),\n",
    "                  loss=BinaryCrossentropy(),\n",
    "                  metrics=[AUC(name='auc')])  # Use AUC as a metric\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "7orWZVCppQR-"
   },
   "outputs": [],
   "source": [
    "# Surgical Aggregation\n",
    "\n",
    "def surgical_aggregation(nodes, max_label, global_model):\n",
    "\n",
    "  \"\"\"\n",
    "  This function will surgically aggregate the last layer\n",
    "  with the assumption that all the other layers\n",
    "  are part of the representation block.\n",
    "\n",
    "  Args:\n",
    "    nodes: list of nodes with their models and label indices\n",
    "    max_label: maximum number of labels / maximum label indice\n",
    "    global_model: global model to update\n",
    "\n",
    "  \"\"\"\n",
    "\n",
    "  # Extract the models and label indices from each node\n",
    "  models = [node['classifier'] for node in nodes]\n",
    "  labels = [node['label_names'] for node in nodes]\n",
    "\n",
    "  # Identify the common and unique classes across all the models\n",
    "\n",
    "  # Create a global final layer with all the labels\n",
    "  lw = models[0].layers[-1].get_weights() # Get the last layer from one of the models\n",
    "  row_shape = lw[0].shape[0] # Get the shape of the last layer\n",
    "  final_layer_w = np.zeros((lw[0].shape[0],max_label+1)) # Use the last layer's shape to initialize the final layer for the global model\n",
    "  final_layer_b = np.zeros(max_label+1) # Same for the bias\n",
    "\n",
    "  node_count = np.zeros(max_label+1) # Number of contributing to each label\n",
    "\n",
    "\n",
    "\n",
    "  num_layers = len(models[0].layers) # Get the number of layers\n",
    "  for iter,layer in enumerate(models[0].layers): # iteratre through all the layers\n",
    "\n",
    "    if isinstance(layer, tf.keras.layers.Dense): # Check if the layer type is a dense layer\n",
    "\n",
    "      if iter == num_layers-1: # Check if this is the final layer (for surgical aggregation)\n",
    "\n",
    "        # Surgically Aggregate the final layer\n",
    "\n",
    "\n",
    "        node_count = np.zeros(max_label+1) # Initialize an array to count the number of nodes contributing to each label\n",
    "        for iter1,model in enumerate(models): # iterate through all the models\n",
    "          w = (model.layers[iter].get_weights()) # get the weights for the last layer\n",
    "          w_0 = w[0] # get the weight matrix\n",
    "          w_1 = w[1] # get the bias array\n",
    "\n",
    "          for l_iter,l in enumerate(labels[iter1]): #iterate through all the labels in this model\n",
    "\n",
    "            final_layer_w[:,l] += w_0[:,l_iter] # FedAvg on each label this model is contributing to\n",
    "            final_layer_b[l] += w_1[l_iter]\n",
    "            node_count[l] += 1\n",
    "\n",
    "        for l in range(max_label+1):\n",
    "          if node_count[l] > 0: # if this label has atleast one contributor\n",
    "            final_layer_w[:,l] /= node_count[l] # perform fedavg\n",
    "            final_layer_b[l] /= node_count[l] # perform fedavg\n",
    "\n",
    "        for iter1,model in enumerate(models):\n",
    "          fw = final_layer_w[:,labels[iter1]] # reassign the weights to each model\n",
    "          fb = final_layer_b[labels[iter1]]\n",
    "          model.layers[iter].set_weights([fw,fb])\n",
    "        global_model.layers[iter].set_weights([final_layer_w,final_layer_b]) # assign the weights to the global model\n",
    "\n",
    "      else:\n",
    "        # Normal FedAvg for all other layers\n",
    "        d_w = [] # for weights\n",
    "        d_b = [] # for bias\n",
    "        for model in models:\n",
    "          d1 = model.layers[iter].get_weights()\n",
    "          d_w.append(d1[0])\n",
    "          d_b.append(d1[1])\n",
    "\n",
    "        new_w = np.mean(d_w,axis=0)\n",
    "        new_b = np.mean(d_b,axis=0)\n",
    "        new_d = [new_w,new_b]\n",
    "\n",
    "        for model in models:\n",
    "          model.layers[iter].set_weights(new_d)\n",
    "        global_model.layers[iter].set_weights(new_d)\n",
    "\n",
    "  # Reassign the models to nodes\n",
    "  for iter,node in enumerate(nodes):\n",
    "    node['classifier'] = models[iter]\n",
    "\n",
    "  return global_model, nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "25u_CTeKIoXo"
   },
   "outputs": [],
   "source": [
    "# Function to create the datasets for FL\n",
    "def create_FL_Datasets(X_train, y_train, n_nodes, labels_shared):\n",
    "\n",
    "\n",
    "  # Initialize the sample and label indices\n",
    "  sample_indices = np.arange(0, X_train.shape[0])\n",
    "  label_indices = np.arange(0, y_train.shape[1])\n",
    "\n",
    "  # Let's shuffle the indices\n",
    "  np.random.shuffle(label_indices)\n",
    "  np.random.shuffle(sample_indices)\n",
    "\n",
    "  # Picking the shared labels\n",
    "  if labels_shared > 0:\n",
    "    shared_labels = label_indices[:labels_shared]\n",
    "    label_indices = label_indices[labels_shared:]\n",
    "  else:\n",
    "    shared_labels = []\n",
    "\n",
    "\n",
    "\n",
    "  # Split the remaining labels into different nodes.\n",
    "  label_indices_per_node = np.array_split(label_indices, n_nodes)\n",
    "\n",
    "\n",
    "  # Split the dataset into different nodes\n",
    "  sample_indices_per_node = np.array_split(sample_indices, n_nodes)\n",
    "\n",
    "  # Let's create the nodes. Each node will store a dictionary of X_train, y_train,\n",
    "  # and label_names\n",
    "  nodes = []\n",
    "  for i in range(n_nodes):\n",
    "    node = {}\n",
    "    node['X_train'] = X_train[sample_indices_per_node[i]]\n",
    "    if len(shared_labels) > 0:\n",
    "      node['y_train'] = y_train[np.ix_(sample_indices_per_node[i],np.concatenate((shared_labels,label_indices_per_node[i])))]\n",
    "      node['label_names'] = np.concatenate((shared_labels,label_indices_per_node[i]))\n",
    "    else:\n",
    "      node['y_train'] = y_train[np.ix_(sample_indices_per_node[i],label_indices_per_node[i])]\n",
    "      node['label_names'] = label_indices_per_node[i]\n",
    "\n",
    "    node['classifier'] = create_mlp_model(node['X_train'].shape[1], node['y_train'].shape[1])\n",
    "    nodes.append(node)\n",
    "\n",
    "  return nodes\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "rWqjLQkUMepG"
   },
   "outputs": [],
   "source": [
    "def get_metrics(model, X_test, y_test, labels):\n",
    "    import numpy as np\n",
    "    from sklearn.metrics import accuracy_score, recall_score, roc_auc_score, roc_curve\n",
    "\n",
    "    # Predict probabilities\n",
    "    y_pred_proba = model.predict(X_test)\n",
    "    y_true = y_test.copy()\n",
    "\n",
    "    # Metrics calculation\n",
    "    metrics = {}\n",
    "    for i in range(labels.shape[1]):\n",
    "        # Calculate the ROC curve to find optimal threshold\n",
    "        fpr, tpr, thresholds = roc_curve(y_true[:, i], y_pred_proba[:, i])\n",
    "        # Calculate Youden's J statistic for each threshold\n",
    "        J = tpr - fpr\n",
    "        optimal_idx = np.argmax(J)\n",
    "        optimal_threshold = thresholds[optimal_idx]\n",
    "\n",
    "        # Apply the optimal threshold to get binary predictions\n",
    "        y_pred = (y_pred_proba[:, i] >= optimal_threshold).astype(int)\n",
    "\n",
    "        # Accuracy\n",
    "        accuracy = accuracy_score(y_true[:, i], y_pred)\n",
    "\n",
    "        # Sensitivity (Recall for the positive class)\n",
    "        sensitivity = recall_score(y_true[:, i], y_pred, pos_label=1)\n",
    "\n",
    "        # Specificity: Recall for the negative class (pos_label=0)\n",
    "        specificity = recall_score(y_true[:, i], y_pred, pos_label=0)\n",
    "\n",
    "        # AUC (Area Under the ROC Curve)\n",
    "        auc = roc_auc_score(y_true[:, i], y_pred_proba[:, i])\n",
    "\n",
    "        # Store metrics for this label\n",
    "        metrics[f'Label_{i}'] = {\n",
    "            'Optimal Threshold': optimal_threshold,\n",
    "            'Accuracy': accuracy,\n",
    "            'Sensitivity': sensitivity,\n",
    "            'Specificity': specificity,\n",
    "            'AUC': auc\n",
    "        }\n",
    "\n",
    "    # Overall metrics\n",
    "    metrics['Overall'] = {\n",
    "        'Accuracy': np.mean([m['Accuracy'] for m in metrics.values() if isinstance(m, dict)]),\n",
    "        'Sensitivity': np.mean([m['Sensitivity'] for m in metrics.values() if isinstance(m, dict)]),\n",
    "        'Specificity': np.mean([m['Specificity'] for m in metrics.values() if isinstance(m, dict)]),\n",
    "        'AUC': np.mean([m['AUC'] for m in metrics.values() if isinstance(m, dict)])\n",
    "    }\n",
    "\n",
    "    return metrics\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def old_get_metrics(model, X_test, y_test, labels, threshold=0.5):\n",
    "        # Predict probabilities\n",
    "        y_pred_proba = model.predict(X_test)\n",
    "        \n",
    "        # Apply consistent threshold\n",
    "        y_pred = (y_pred_proba >= threshold).astype(int)\n",
    "        y_true = y_test.copy()\n",
    "\n",
    "        # Metrics calculation\n",
    "        metrics = {}\n",
    "        for i in range(labels.shape[1]):\n",
    "            metrics[f'Label_{i}'] = {\n",
    "                'Accuracy': accuracy_score(y_true[:, i], y_pred[:, i]),\n",
    "                'Sensitivity': recall_score(y_true[:, i], y_pred[:, i], pos_label=1),\n",
    "                'Specificity': recall_score(y_true[:, i], y_pred[:, i], pos_label=0),\n",
    "                'AUC': roc_auc_score(y_true[:, i], y_pred_proba[:, i])\n",
    "            }\n",
    "        \n",
    "        # Overall metrics\n",
    "        metrics['Overall'] = {\n",
    "            'Accuracy': np.mean([m['Accuracy'] for m in metrics.values() if isinstance(m, dict)]),\n",
    "            'Sensitivity': np.mean([m['Sensitivity'] for m in metrics.values() if isinstance(m, dict)]),\n",
    "            'AUC': np.mean([m['AUC'] for m in metrics.values() if isinstance(m, dict)])\n",
    "        }\n",
    "        \n",
    "        return metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lz24rDr6T5zt"
   },
   "source": [
    "# Experiment 1: Varying the number of nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hA54drxFqRjH",
    "outputId": "5ce0950a-4b6b-4ca7-a7c1-5f17402ccb49"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - auc: 0.6034 - loss: 0.6886  \n",
      "2 0\n",
      "2 1\n",
      "2 2\n",
      "2 3\n",
      "2 4\n",
      "2 5\n",
      "2 6\n",
      "2 7\n",
      "2 8\n",
      "2 9\n",
      "2 10\n",
      "2 11\n",
      "2 12\n",
      "2 13\n",
      "2 14\n",
      "2 15\n",
      "2 16\n",
      "2 17\n",
      "2 18\n",
      "2 19\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - auc: 0.9737 - loss: 0.0912 \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "performance of the global model after training: AUC= 0.9714391231536865 Loss= 0.0961718037724495\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - auc: 0.5222 - loss: 0.6929  \n",
      "3 0\n",
      "3 1\n",
      "3 2\n",
      "3 3\n",
      "3 4\n",
      "3 5\n",
      "3 6\n",
      "3 7\n",
      "3 8\n",
      "3 9\n",
      "3 10\n",
      "3 11\n",
      "3 12\n",
      "3 13\n",
      "3 14\n",
      "3 15\n",
      "3 16\n",
      "3 17\n",
      "3 18\n",
      "3 19\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - auc: 0.9631 - loss: 0.1006 \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "performance of the global model after training: AUC= 0.966762363910675 Loss= 0.10045429319143295\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - auc: 0.3958 - loss: 0.6895  \n",
      "4 0\n",
      "4 1\n",
      "4 2\n",
      "4 3\n",
      "4 4\n",
      "4 5\n",
      "4 6\n",
      "4 7\n",
      "4 8\n",
      "4 9\n",
      "4 10\n",
      "4 11\n",
      "4 12\n",
      "4 13\n",
      "4 14\n",
      "4 15\n",
      "4 16\n",
      "4 17\n",
      "4 18\n",
      "4 19\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - auc: 0.9526 - loss: 0.1238 \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "performance of the global model after training: AUC= 0.9545574188232422 Loss= 0.12366596609354019\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - auc: 0.3335 - loss: 0.6858  \n",
      "5 0\n",
      "5 1\n",
      "5 2\n",
      "5 3\n",
      "5 4\n",
      "5 5\n",
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      "5 19\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - auc: 0.9528 - loss: 0.1205 \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "performance of the global model after training: AUC= 0.9515061378479004 Loss= 0.12378102540969849\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - auc: 0.6590 - loss: 0.6928  \n",
      "6 0\n",
      "6 1\n",
      "6 2\n",
      "6 3\n",
      "6 4\n",
      "6 5\n",
      "6 6\n",
      "6 7\n",
      "6 8\n",
      "6 9\n",
      "6 10\n",
      "6 11\n",
      "6 12\n",
      "6 13\n",
      "6 14\n",
      "6 15\n",
      "6 16\n",
      "6 17\n",
      "6 18\n",
      "6 19\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - auc: 0.9374 - loss: 0.1383 \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "performance of the global model after training: AUC= 0.9344518780708313 Loss= 0.14191551506519318\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - auc: 0.4841 - loss: 0.6953  \n",
      "7 0\n",
      "7 1\n",
      "7 2\n",
      "7 3\n",
      "7 4\n",
      "7 5\n",
      "7 6\n",
      "7 7\n",
      "7 8\n",
      "7 9\n",
      "7 10\n",
      "7 11\n",
      "7 12\n",
      "7 13\n",
      "7 14\n",
      "7 15\n",
      "7 16\n",
      "7 17\n",
      "7 18\n",
      "7 19\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - auc: 0.9297 - loss: 0.1536 \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
      "performance of the global model after training: AUC= 0.9315657019615173 Loss= 0.15705394744873047\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - auc: 0.4646 - loss: 0.6924  \n",
      "8 0\n",
      "8 1\n",
      "8 2\n",
      "8 3\n",
      "8 4\n",
      "8 5\n",
      "8 6\n",
      "8 7\n",
      "8 8\n",
      "8 9\n",
      "8 10\n",
      "8 11\n",
      "8 12\n",
      "8 13\n",
      "8 14\n",
      "8 15\n",
      "8 16\n",
      "8 17\n",
      "8 18\n",
      "8 19\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - auc: 0.9244 - loss: 0.1589 \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
      "performance of the global model after training: AUC= 0.92341148853302 Loss= 0.16227206587791443\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - auc: 0.5447 - loss: 0.6929  \n",
      "9 0\n",
      "9 1\n",
      "9 2\n",
      "9 3\n",
      "9 4\n",
      "9 5\n",
      "9 6\n",
      "9 7\n",
      "9 8\n",
      "9 9\n",
      "9 10\n",
      "9 11\n",
      "9 12\n",
      "9 13\n",
      "9 14\n",
      "9 15\n",
      "9 16\n",
      "9 17\n",
      "9 18\n",
      "9 19\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - auc: 0.9065 - loss: 0.1757 \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "performance of the global model after training: AUC= 0.9091029763221741 Loss= 0.1786370724439621\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - auc: 0.5202 - loss: 0.6893  \n",
      "10 0\n",
      "10 1\n",
      "10 2\n",
      "10 3\n",
      "10 4\n",
      "10 5\n",
      "10 6\n",
      "10 7\n",
      "10 8\n",
      "10 9\n",
      "10 10\n",
      "10 11\n",
      "10 12\n",
      "10 13\n",
      "10 14\n",
      "10 15\n",
      "10 16\n",
      "10 17\n",
      "10 18\n",
      "10 19\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - auc: 0.9037 - loss: 0.1757 \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "performance of the global model after training: AUC= 0.9057980179786682 Loss= 0.17845752835273743\n"
     ]
    }
   ],
   "source": [
    "# Federated Learning Experiment\n",
    "\n",
    "# Create train and test sets\n",
    "test_size = 0.2\n",
    "X_train, X_test, y_train, y_test = train_test_split(allEmbeddings, labels, test_size=test_size, random_state=42)\n",
    "\n",
    "# Input parameters\n",
    "\n",
    "labels_shared = 0\n",
    "\n",
    "num_epochs = 1\n",
    "num_rounds = 20\n",
    "\n",
    "allGlobalModels = []\n",
    "allPerf = []\n",
    "allLabelNames = []\n",
    "allMetrics = []\n",
    "\n",
    "for n_nodes in range(2, 11):\n",
    "\n",
    "  # Create FL datasets\n",
    "  nodes = create_FL_Datasets(X_train, y_train, n_nodes, labels_shared)\n",
    "\n",
    "  # Initialize the global model\n",
    "  global_model = create_mlp_model(allEmbeddings.shape[1], labels.shape[1])\n",
    "  max_label = labels.shape[1]-1\n",
    "  perf = global_model.evaluate(X_test,y_test)\n",
    "\n",
    "  ### Perform federated learning\n",
    "  # Iteratively train all models for N epochs and merge them using surgical aggregation\n",
    "\n",
    "  for round in range(num_rounds):\n",
    "    for node in nodes:\n",
    "      node['classifier'].fit(node['X_train'], node['y_train'], epochs=num_epochs, verbose=0)\n",
    "\n",
    "    print (n_nodes,round)\n",
    "\n",
    "    # surgical aggregation FL\n",
    "    global_model, nodes = surgical_aggregation(nodes, max_label, global_model)\n",
    "\n",
    "  perf = global_model.evaluate(X_test,y_test)\n",
    "  allGlobalModels.append(global_model)\n",
    "  allPerf.append(perf)\n",
    "  label_names = [node['label_names'] for node in nodes]\n",
    "  allLabelNames.append(label_names)\n",
    "  allMetrics.append(get_metrics(global_model, X_test, y_test, labels))\n",
    "\n",
    "  print(\"performance of the global model after training: AUC=\",perf[1], \"Loss=\", perf[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "U54KZxzR3ssA",
    "outputId": "db228d35-e4cf-4f4d-bae4-b06cd95351f3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - auc: 0.9792 - loss: 0.0864  \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n"
     ]
    }
   ],
   "source": [
    "# Train and evaluate the base model\n",
    "base_model = create_mlp_model(allEmbeddings.shape[1], labels.shape[1])\n",
    "\n",
    "base_model.fit(X_train, y_train, epochs=20, verbose=0)\n",
    "base_perf = base_model.evaluate(X_test,y_test)\n",
    "base_metrics = get_metrics(base_model, X_test, y_test, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "id": "MvaiwvWb1-EJ",
    "outputId": "b19a946a-570a-4427-8d52-a5aa898857b5"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot AUC vs. number of nodes using seaborn\n",
    "perf_AUC = [x[1] for x in allPerf]\n",
    "n_nodes = range(2,11)\n",
    "plt.figure(figsize=(8, 5))\n",
    "\n",
    "plt.errorbar(\n",
    "    n_nodes,        # x-values\n",
    "    perf_AUC,       # y-values (the means)\n",
    "    yerr=np.std(perf_AUC, axis=0),   # error (standard deviation)\n",
    "    fmt='-o',       # line and marker style\n",
    "    capsize=5,      # adds \"caps\" to the error bars\n",
    "    label='Federated Learning'\n",
    ")\n",
    "\n",
    "# Base model is a constant baseline (no error bars needed):\n",
    "plt.plot(\n",
    "    n_nodes,\n",
    "    [base_perf[1]] * len(n_nodes),\n",
    "    'r--',\n",
    "    label='Base Model'\n",
    ")\n",
    "\n",
    "plt.xlabel('Number of Nodes')\n",
    "plt.ylabel('AUC')\n",
    "plt.title('AUC vs Number of Nodes')\n",
    "plt.ylim(0, 1)\n",
    "plt.xticks(n_nodes)\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "id": "xkchnL9m3CPw",
    "outputId": "119193fe-4411-49b0-8136-f6fdcf86f77a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Mean Accuracy vs. number of nodes\n",
    "perf_Accuracy = [x['Overall']['Accuracy'] for x in allMetrics]\n",
    "\n",
    "# For the base model\n",
    "base_overall_accuracy = base_metrics['Overall']['Accuracy']\n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "\n",
    "plt.errorbar(\n",
    "    n_nodes,\n",
    "    perf_Accuracy,\n",
    "    yerr=np.std(perf_Accuracy, axis=0),\n",
    "    fmt='-o',\n",
    "    capsize=5,\n",
    "    label='Federated Learning'\n",
    ")\n",
    "\n",
    "plt.plot(\n",
    "    n_nodes,\n",
    "    [base_overall_accuracy] * len(n_nodes),\n",
    "    'r--',\n",
    "    label='Base Model'\n",
    ")\n",
    "\n",
    "plt.xlabel('Number of Nodes')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Accuracy vs Number of Nodes')\n",
    "plt.ylim(0.0, 1.0)\n",
    "plt.xticks(n_nodes)\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Storing Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment_directory = os.path.join('data', 'Experiment 1')\n",
    "\n",
    "os.makedirs(experiment_directory, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert any NumPy arrays to lists\n",
    "allPerf_serializable = [perf.tolist() if isinstance(perf, np.ndarray) else perf for perf in allPerf]\n",
    "\n",
    "# Save to JSON\n",
    "with open(os.path.join(experiment_directory, 'allPerf.json'), 'w') as f:\n",
    "    json.dump(allPerf_serializable, f)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to make data JSON serializable\n",
    "def make_serializable(obj):\n",
    "    if isinstance(obj, dict):\n",
    "        return {k: make_serializable(v) for k, v in obj.items()}\n",
    "    elif isinstance(obj, list):\n",
    "        return [make_serializable(v) for v in obj]\n",
    "    elif isinstance(obj, np.ndarray):\n",
    "        return obj.tolist()\n",
    "    elif isinstance(obj, (np.float32, np.float64, np.int32, np.int64)):\n",
    "        return obj.item()  # Convert NumPy scalars to native Python types\n",
    "    else:\n",
    "        return obj\n",
    "\n",
    "# Convert allMetrics to a serializable format\n",
    "allMetrics_serializable = make_serializable(allMetrics)\n",
    "\n",
    "# Save to JSON\n",
    "with open(os.path.join(experiment_directory, 'allMetrics.json'), 'w') as f:\n",
    "    json.dump(allMetrics_serializable, f)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert NumPy array to list if necessary\n",
    "base_perf_serializable = base_perf.tolist() if isinstance(base_perf, np.ndarray) else base_perf\n",
    "\n",
    "# Save to JSON\n",
    "with open(os.path.join(experiment_directory, 'base_perf.json'), 'w') as f:\n",
    "    json.dump(base_perf_serializable, f)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert base_metrics to a serializable format\n",
    "base_metrics_serializable = make_serializable(base_metrics)\n",
    "\n",
    "# Save to JSON\n",
    "with open(os.path.join(experiment_directory, 'base_metrics.json'), 'w') as f:\n",
    "    json.dump(base_metrics_serializable, f)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Storing Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "models_directory = os.path.join(experiment_directory, 'models')\n",
    "os.makedirs(models_directory, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save each global model's architecture and weights\n",
    "for idx, model in enumerate(allGlobalModels):\n",
    "    # Save model architecture to JSON\n",
    "    model_json = model.to_json()\n",
    "    with open(os.path.join(models_directory, f'allGlobalModels_model_{idx}.json'), 'w') as json_file:\n",
    "        json_file.write(model_json)\n",
    "    # Save model weights to HDF5\n",
    "    model.save_weights(os.path.join(models_directory, f'allGlobalModels_{idx}.weights.h5'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save base model architecture to JSON\n",
    "base_model_json = base_model.to_json()\n",
    "with open(os.path.join(models_directory, 'base_model.json'), 'w') as json_file:\n",
    "    json_file.write(base_model_json)\n",
    "\n",
    "# Save base model weights to HDF5\n",
    "base_model.save_weights(os.path.join(models_directory, 'base_model.weights.h5'))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiment 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the output directory if it doesn't exist\n",
    "output_dir = 'data/Experiment 2'\n",
    "if not os.path.exists(output_dir):\n",
    "    os.makedirs(output_dir)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_serializable(obj):\n",
    "    if isinstance(obj, dict):\n",
    "        return {k: make_serializable(v) for k, v in obj.items()}\n",
    "    elif isinstance(obj, list):\n",
    "        return [make_serializable(v) for v in obj]\n",
    "    elif isinstance(obj, np.ndarray):\n",
    "        return obj.tolist()\n",
    "    elif isinstance(obj, np.integer):\n",
    "        return int(obj)\n",
    "    elif isinstance(obj, np.floating):\n",
    "        return float(obj)\n",
    "    else:\n",
    "        return obj\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Experiment parameters\n",
    "num_iterations = 20\n",
    "num_rounds = 20\n",
    "num_epochs = 1\n",
    "labels_shared = 0\n",
    "test_size = 0.2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running iteration 1...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
      "Running iteration 2...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "Running iteration 3...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "Running iteration 4...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "Running iteration 5...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
      "Running iteration 6...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "Running iteration 7...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "Running iteration 8...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "Running iteration 9...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "Running iteration 10...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "Running iteration 11...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "Running iteration 12...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "Running iteration 13...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "Running iteration 14...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "Running iteration 15...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "Running iteration 16...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "Running iteration 17...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "Running iteration 18...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "Running iteration 19...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "Running iteration 20...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "All 20 iterations are complete, and results are saved.\n"
     ]
    }
   ],
   "source": [
    "all_experiment_results = []\n",
    "\n",
    "for iteration in range(num_iterations):\n",
    "    print(f\"Running iteration {iteration + 1}...\")\n",
    "    \n",
    "    # Set a different random state for each iteration\n",
    "    random_state = np.random.randint(10000)\n",
    "    \n",
    "    # Train-test split with a new random state each time\n",
    "    X_train, X_test, y_train, y_test = train_test_split(\n",
    "        allEmbeddings, labels, test_size=test_size, random_state=random_state)\n",
    "    \n",
    "    # Initialize containers to store results for this iteration\n",
    "    iteration_global_models = []\n",
    "    iteration_perf = []\n",
    "    iteration_label_names = []\n",
    "    iteration_metrics = []\n",
    "    \n",
    "    # Federated Learning Experiment for this iteration\n",
    "    for n_nodes in range(2, 11):\n",
    "        nodes = create_FL_Datasets(X_train, y_train, n_nodes, labels_shared)\n",
    "        global_model = create_mlp_model(allEmbeddings.shape[1], labels.shape[1])\n",
    "        max_label = labels.shape[1] - 1\n",
    "        \n",
    "        # Perform federated learning\n",
    "        for round in range(num_rounds):\n",
    "            for node in nodes:\n",
    "                node['classifier'].fit(node['X_train'], node['y_train'], epochs=num_epochs, verbose=0)\n",
    "            global_model, nodes = surgical_aggregation(nodes, max_label, global_model)\n",
    "        \n",
    "        # Evaluate the global model\n",
    "        perf = global_model.evaluate(X_test, y_test, verbose=0)\n",
    "        iteration_global_models.append(global_model)\n",
    "        iteration_perf.append(perf)\n",
    "        label_names = [node['label_names'].tolist() for node in nodes]\n",
    "        iteration_label_names.append(label_names)\n",
    "        metrics = get_metrics(global_model, X_test, y_test, labels)\n",
    "        iteration_metrics.append(metrics)\n",
    "    \n",
    "    # Convert data to JSON-serializable format\n",
    "    iteration_perf_serializable = make_serializable(iteration_perf)\n",
    "    iteration_label_names_serializable = make_serializable(iteration_label_names)\n",
    "    iteration_metrics_serializable = make_serializable(iteration_metrics)\n",
    "    \n",
    "    # Store results of this iteration in a specific folder\n",
    "    iteration_dir = os.path.join(output_dir, f'iteration_{iteration + 1}')\n",
    "    os.makedirs(iteration_dir, exist_ok=True)\n",
    "    \n",
    "    # Save performance metrics as JSON\n",
    "    with open(os.path.join(iteration_dir, 'iteration_perf.json'), 'w') as f:\n",
    "        json.dump(iteration_perf_serializable, f)\n",
    "    \n",
    "    # Save label names as JSON\n",
    "    with open(os.path.join(iteration_dir, 'iteration_label_names.json'), 'w') as f:\n",
    "        json.dump(iteration_label_names_serializable, f)\n",
    "    \n",
    "    # Save detailed metrics as JSON\n",
    "    with open(os.path.join(iteration_dir, 'iteration_metrics.json'), 'w') as f:\n",
    "        json.dump(iteration_metrics_serializable, f)\n",
    "    \n",
    "    # Save global models\n",
    "    models_dir = os.path.join(iteration_dir, 'models')\n",
    "    os.makedirs(models_dir, exist_ok=True)\n",
    "    for idx, model in enumerate(iteration_global_models):\n",
    "        # Save model architecture to JSON\n",
    "        model_json = model.to_json()\n",
    "        with open(os.path.join(models_dir, f'global_model_{idx}.json'), 'w') as json_file:\n",
    "            json_file.write(model_json)\n",
    "        # Save model weights (weights cannot be saved as JSON)\n",
    "        model.save_weights(os.path.join(models_dir, f'global_model_{idx}.weights.h5'))\n",
    "    \n",
    "    # Save iteration-level data for comparison\n",
    "    experiment_data = {\n",
    "        'perf': iteration_perf_serializable,\n",
    "        'label_names': iteration_label_names_serializable,\n",
    "        'metrics': iteration_metrics_serializable,\n",
    "        'random_state': random_state\n",
    "    }\n",
    "    all_experiment_results.append(experiment_data)\n",
    "\n",
    "# Save all experiment results as JSON\n",
    "all_experiment_results_serializable = make_serializable(all_experiment_results)\n",
    "with open(os.path.join(output_dir, 'all_experiment_results.json'), 'w') as f:\n",
    "    json.dump(all_experiment_results_serializable, f)\n",
    "\n",
    "print(\"All 20 iterations are complete, and results are saved.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.09137485176324844, 0.9733574986457825], [0.10791409760713577, 0.960588812828064], [0.11751870810985565, 0.965496301651001], [0.11978013813495636, 0.9572921991348267], [0.13073185086250305, 0.9538974761962891], [0.15429148077964783, 0.9382579326629639], [0.1524856835603714, 0.937287449836731], [0.1626526117324829, 0.9329901933670044], [0.1807689517736435, 0.9213055372238159]]\n"
     ]
    }
   ],
   "source": [
    "with open('data/Experiment 2/all_experiment_results.json', 'r') as f:\n",
    "    all_experiment_results = json.load(f)\n",
    "\n",
    "# Access specific iteration results, for example, iteration 1:\n",
    "iteration_1_results = all_experiment_results[0]  # Index starts at 0\n",
    "\n",
    "# Print performance metrics for iteration 1\n",
    "print(iteration_1_results['perf'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the directory where the experiment data is stored\n",
    "experiment_dir = 'data/Experiment 2'\n",
    "\n",
    "# Load the overall results of all experiments\n",
    "with open(os.path.join(experiment_dir, 'all_experiment_results.json'), 'r') as f:\n",
    "    all_experiment_results = json.load(f)\n",
    "\n",
    "\n",
    "all_auc = []      # To store AUC values across iterations\n",
    "all_accuracy = [] # To store accuracy values across iterations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "for experiment in all_experiment_results:\n",
    "    perf_metrics = experiment['perf']\n",
    "    auc_values = [perf[1] for perf in perf_metrics]  # Assuming AUC is at index 1 in 'perf'\n",
    "    # Extract 'Accuracy' from 'metrics' dictionary for each node\n",
    "    accuracy_values = [metric['Overall']['Accuracy'] for metric in experiment['metrics']]\n",
    "    \n",
    "    all_auc.append(auc_values)\n",
    "    all_accuracy.append(accuracy_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert lists to NumPy arrays for easy calculation of mean and std dev\n",
    "all_auc = np.array(all_auc)\n",
    "all_accuracy = np.array(all_accuracy)\n",
    "\n",
    "# Calculate mean and standard deviation for AUC and Accuracy across all iterations\n",
    "mean_auc = np.mean(all_auc, axis=0)\n",
    "std_auc = np.std(all_auc, axis=0)\n",
    "\n",
    "mean_accuracy = np.mean(all_accuracy, axis=0)\n",
    "std_accuracy = np.std(all_accuracy, axis=0)\n",
    "\n",
    "# Define the number of nodes used during the experiment\n",
    "n_nodes = np.arange(2, 11)  # Node range from 2 to 10 inclusive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AE0pBhw74QrtPbQ+N2dKBdnFjnYorh96DunF5873/ofWVkZHhlgukOtN3LLS+WrZs6eZ7dabAXd3o1E1JY/IUBOuEQqQgStuSuvF63fL0XweSOuDVPkLd6TQuUd+1HQ2c9txzz6DHXjbDaL7/Bx98sNtvadnZs2e7rn76zmofpvJFopNG6r6l7VmBjL5/qkd1OQv3/YtU1uI+Q60zNEujPgN1GdOyga+tMYXhXlv7iUDFvVZx2/z28IIe7wRSab6r2vZUv14Qq3UpgPHGEZZE49F0gkPjizTuTOtXcFPafWJgXWmfo6A4kAI+fXdDx6GGfs6l+U0CdjbSoAA7mVqYdPZ71apV7oC+uMxi+qFX0FRcQOIN1tVBh/qPKzBQ64cOlPUDqLOFGrsUetBWXF/3aFpNvLIUN8ZCZwi9g06VQWM9tN7QH25N0zicwIxgOpuvIERnkLc385PGtOjAUgerGkejA86lS5cWyVKlfvRqWVDLgMYtaKyN+t9reQUvpWl10lgI1bvGO4Qq7oAlNGlHpM9mRz6z0DJoe/ASDIRbb+g1kQLPupcFXquTAqfiWp1iTXWmpAIaYxKOF9iqrtRqpJYAHXhOnDjRBfFqmdJ2VtznKAqSNJ5F3wsFThqvpH2DEhDosQIx2dHAaUe2JY++nwqidFPwqFYsnaDQWKSSqGVozJgx7vNToKjPUtuiAsxwwWUsyurROCWNhbr44ovdd1atojq4V1nCvXYitntv/JgXcJTmu3rooYe6cbTa9+k3RmObFEgFjsELR4kntC/U/kvjvvSbo9fS/jCw5X57RJtxNJafMxBvBE7ATqaWIQ2W1kG61zISjgIgdUVRkFLSj7iyQKnbic4EB17PZfHixTEtt9angd0anO1lRfPoB14XcVRLmpf5ToPJ1eVDP76hZx7VUqXgIfCipxp0r65Ab775ZlBXwNLSgYIydy1YsMDVrzJiad2hdCCsm8rrJTAYNWqUy9BV2lYnLwgL5Z31Dk24EC7zX6zoLHXg2XLVtT4fL7uatisdkGgZr8UkFrzPUvUeSt1FdYZ/R1PWe3Swq6QHyvAVeuIhsByBLUcKyLUNq8U3cDnVl9flTtSiquWUbMCjOlP3NGWgi3QwqINxLaebAi0dsCsIUjDlvXY4CohURiWPUXZJL0DSd9oLnPR5eQFUcXZ2enx16fK6skUqgzJEqqubTlx4FChu74WtAz/DwM9ayQ1CWyv02kpsooQggfTa2jZL81qhwm3zpaX9ofaf2l95XetK+11Vlz4lB1K3Z+379J1XQFUS1YvqTr8fgZ9baBBcmu1KdaV9jupKrc2BFzdXfRd3sWugPKCrHrCT6SyhMhTpjHm4A/rAH0H9mOrsaCgFJN7Bhne2LvDsnA7AlI45lrzWJqVgVtecwJvKqmAqsHVMrWmi9Lqh1DUucBnRWJOGDRu6fvC//PJLkeeoW0o0QY3GXKlOdACqs+DKohV4wK6DCtVfIAVQOuANTZUbDXWJVGCog/hQOvARb+yK6DNVCux48erWowxwgXWtTFiqH5U39IyuHisI3x767JTRTd1+Ag+EdRZdrS3K8BfrVicFq17mL4+CE7WIKGtb4PvTAbO6Hiltv3fAr1ZbBcv6vgRmqAw9kNf2rWAm3HXEdFbf64KornShVCcSadtSd1d1E1TrqFpDlPpbFEDpJIvS0UfT2qRtfXu7WJVEgV+4FgBvPEtgdzWVIVwwpO0udB3aPotrgY1En7XqTOsIXG+4CwCHe23tH/S5lnb7DqxfpZ+PpptiSfT+1eqt7nf673UrLu13VSeNtJ2pjGrtjOaC4eF+P5T2XiexAnnXbosmyPW+66Gfg9di630HgfKIFicgAXTWNRIFImqZUpcJHRwqxa4OEnQWTz/4OrOooOWwww5zLRtap350dWZQqWJj3c1BQZEOHELH23g0aFxdcbwLlGpZjfVQOVVmDaL2DjR0sKV5gWf19R6UJlk/unquAhIlwhCtU4GQuvdEoq4mOrOsH2mNvwjtqqLrDKnVTH3/dRZXQZTqSwcQCrpKS89Ti0K4BAo6+NUZX40F0UG1Doh1fZjQwC2W1Fqiz0Ip53Xwo6446rrj1bWCOQWgKpPGPamLjsZU6Hmqfw3IVwrm7aF00ArQ9DldcsklLqjQQa0CnWivv1SasU4as6KWoMDAWMGQ3psONlUHqgu1COhEgrqWeWP/9F1SPeg7phYnbSeqA3UlCx3jpNZUdYFScK8AQq2TOthVS5qmK9GIAjF12VSQrANDnVVXsK/XVffPkgboewem2t4VJHnXcPJanBSY6RZN4KR1qLVB4/j0fnWipqQTNNHSd1tJKtRirm61CjbVUuu1bARu/yqDWsv1HVT3WbWYKDDUSQx917Q9KCGAtk8tp3E120OftbZV7SO1bu071H1TXdtCW5E0X5+Pyql9plKRa58W+lmXRK+jz1afpbr86Tut7Vvf88CkDCVR0KXvpKg+1SKs1h61zKvLYuCJstJ+V7Xf1Ukc7Y8UQEXqpufVi15fn6vem9atkwn6fALfk3o9aJo+b+03tS9TN1LdQmlfo98jnSDyupJrTJwCOr2Hki5pAJR5CcvnB1TAdOQlCU1HHpgeWSm2q1Sp4lKZH3DAAS4V8IoVK/zLKB3yoYce6pbZfffd/amCw6W8DpcOV2l7S0qnO2fOHLeuO+64o9hllDpXyyhlsEfpf5VaV2nGK1eu7G66/9hjjxVJDezR+9I6WrZs6ZZXSnW9f6XeXb9+vS8azz77rCuL6uvff/8Nmrdo0SKXZrdFixZu/XXq1HEp4T/55JOI6y0uxXpOTo5bX2g6cvntt99cWmWl11WKX6VKnjRpUtSfTXHbRehreSmBf/rpJ9+ZZ57p3rtSd/ft27dIHcibb77p+7//+z/3fnRr1aqVW9+CBQsilqkkqkelq9e2WLNmTd/JJ5/syhRoe9ORh/Lec7jPROnH9Z4yMjJcvV911VUuzXKoJ5980tesWTP3+XTo0MGlRQ5N2SxKu/3AAw+4+tCyqlttl0OHDvVvl5MnT/adeuqp7juoNNn637NnT98vv/wSVd3ddNNN7v3odQLttddebrq2pXB1E7gdbdy40Xfeeef5ateuHZQmu7h6VEru0NT24Xz44Yfue6M6rV69unt/Kte1117rW716ddCySqN/5JFHum0gMC246r9Pnz6+unXrunV0797dLasyBqYOL26fGe79aj+iz0BpwvV6Rx11lG/evHlF1ql05Epv7S2nbVSp0EM/60jbpr43SumubaB169bucgSR9p+h6f29m+pg77339l1wwQUurXlxovmuem677Ta3bn02xZUh8P0qlfm9997ryq/3pHT377//ftj3NGPGDLfN67MPTE0emo7c2yfqc9F3S9/Bxo0b+wYNGhR0eYyS9m/hvoNAWZCiP4kO3gAAAACgLGOMEwAAAABEQOAEAAAAABEQOAEAAABAWQ6clH1I2X6UdUcZhN55552Iz9E1a5Q5Rlf+VvYYpY4FAAAAgKQNnJReVWkrQ687UhylyVS6TKWyVHpmXcdDKY2VChYAAAAA4qXMZNVTi5OuS6Ac/8W5+eab7X//+5+7oKJH1z3QdQJ0sTcAAAAAsIp+AVxdLE9XCg/UvXt31/JUHF0ELvCK7fn5+e6idbrgnneBQQAAAAAVj8/nsw0bNrihQ6mpqckTOK1atcrq168fNE2Ps7Ky3BXqdWXrcFf61hXkAQAAACCcZcuW2R577GFJEzhtj0GDBtmAAQP8j9evX2977rmnGy9Vo0YNS7ScnBybMmWKG7eVkZGR6OIkHeo3vqjf+KJ+44v6jS/qN76o3/iifitO/W7YsMGaNWsWVVxQrgKnBg0a2OrVq4Om6XHNmjXDtjaJsu/pFqpOnTrueWVhw6latarrOpjoDScZUb/xRf3GF/UbX9RvfFG/8UX9xhf1W3HqN6Pw9aMZwlOuruPUqVMnmzx5ctC0SZMmuekAAAAAEC8JDZw2btzo0orrJuo+p/tLly71d7Pr1auXf/krr7zSFi1aZAMHDrT58+fbk08+aRMmTLD+/fsn7D0AAAAASH4JDZy+/vpra9eunbuJxiLp/uDBg93jlStX+oMoUf9DpSNXK5Ou//Twww/bc8895zLrAQAAAEC8JHSM01FHHeVSABZn7NixYZ/zzTffWDLIy/fZrMV/2zcrcm3Xn1ZYp+a7WlpqSP/KtDSzypW3Pd60qfgVKoVi4Fiv0iy7ebPyMYZfVn0+q1bdvmX//Vc54IsvR7Vq27dsdrZZXl5Uy6Zu3VpQF8X1oVV5vX6tSl2fm1v8ekuzrOrXS2upMuTkxGZZbQ/aLkq7rJbT8sXRWMD09FIvm6LPoaT6zczcNk/1FXB5gBKX1Xr1ORdHy2n50i6rbUzbWiyWVR14Yyj1ndB3IxbLhvveF1e/7CN2eB/hli1pe2cfsX37CI/qoKRl2UeUfll9Zt7nFum7zD5i+/YR3mcczbLsI0q/j4iQ9rvM8lUw69ev1zfV/U+kD39Y4Tv03k98TW5+33879Koxvg9bdtKuZNvthBOCn1i1avD8wFvnzsHL1q1b/LIdOgQv26RJ8cu2bh28rB4Xt6zWE0ivU9yyKl8glb+4ZfW+A6leils2YLPeunWr74/DDit52Y0bt623d++Sl12zZtuyV19d8rKLF29b9sYbS1523rxtyw4ZUvKys2dvW/bBB0tedsqUbcuOHFnysu+/v23ZMWNKXnbCBH/9zr7pppKX1bo8eo2SllUZPSp7ScvqvXtUJyUtqzr1qK5LWlaflUefYUnLahvwaNsoaVltWx5tcyUte+aZ/vp95513Sl6WfcR27yO8+s07/fSSl2UfsV37CK9+c159teRl2Uds1z7Cq1/9L3FZ9hHbtY8Iql/tk0uqY/YRvtLuI7Rf8NdvOYoNymm4V/6sycq2ecvXu9sz036zK1+eayvXB5/9WlVjV7uqx602seW2ZBdbcks4cwIAAABgp0hR9GQViC6WW6tWLXc9p52ZjvzRSb/YiMm/Rl6w8OPIyM0xNeT2aL+HnXfE3lY5I9Uqp6dZ5ZwthfdTrVJ6anDqRJrYg5ZVV8iZC9fYpClfWNdD24fvCik0sW93Vz2lE/3wvffs+GOOKT6dKN1wSr9sYdca1e8HH3xgJ3TuXHz90g1nu/cR/vrV9ltStxH2Edu1j8jx+Qrqt1s3yyjpc2MfUfpl09IsJy2toH5POMEySvos2Eds1z4iJzNzW/1qObrqWSz3ETmpqfbBxx8X1G+C05GXJjYoV9dxKs/O77indW1d377/Y53d+va84hcs/DLlZBTslCd8v8bdiqPgqXJGWkEwpf8KrjJSrZKbpsfb5lcqnFfwOC3guYXT3fzAZbznpFnl7Bz3PyMtNXiHFkkx19fa4WUDfwTCmDhvpQ1976fCVr1K9sLiedawVmUbcnJrO27/hiV/6cNc92uHl9WPbGB/6UQsqx1TtDunKJb1xuh9/U+61VmzxTrtVSt8YBpIO1RvpxqJdtSBP3KxWlY/LPFYVt/deCwrWjbaz660641Wab735WAfUWTZaOuXfUT0y3oHY/rOR7te9hHRLxt4sBuv731F3kcE1m9p9ifsI6JbtqRgrQwjcNrJ/tkc3Yaye63KLlDRwWluvs+yc/IKbrn5blpgVz7d1pdwwiuWdGDsWrsCgrJKQYFXcDDmD7z8gV3R+ZVCgr7QYC410sF4YVfINRsKzlLOWPin3fvh/CLLKIhSF8lbj29lh+1V102rV6OS1atZih0iwgSmafbir19HF5gCAACUUwROO8krs5ZG11Wv0IrC8U/9jt3b+ndtGTQvJy+/MJAq+L8lN/B+8LzswHmFgZc/CPMvEzA/R4FY4Tz3v+C+R0Hbpq157razZKYpUAtoWQsJxhR8/f7XJvt1zcao1hcYVJ3cpqGd2b6xZaSmuKAwPS3VMtJSLD214L+mqZUtPdy0wudEc6XpZAuarnp5roV2sli1PttNf+qCgwieAABA0iFw2sld9RR4XPLCV/bnxuL7fdatnmnP9z7YHZSrRSSUDtp1q7GTGko0DM61bIUEU4GtYN79wGW23Q8I5nK3BWihgZ0L2gqn5eRtOyzfmpfvbhuyS+gHvJ3e+26lu+0IL9BSIOUCLH0+hUGYf1ph0KVpBYFXwLTUVEtLS/E/xz/NWy5gfUWeW+L6CgK+4PUVTiucV1C+otNCg0GvRU/b7+3vzCsSNIk3TfMb1qri335p0QMAAMmAwGkn0cGjdwB5d4/93Zl5CTwA9Q5VNb9N49pWVugg2us+V8t2zgA+HaAHt6AFt4JtCQm8/tywxf7atNUWrM6ymb/9HXH9e+xSxWpUTreUwlrPzc+33LyCbpG5efmWU/jfPy0/PyiYC6TpOSUNGi2HXOtbQKCl+v43J7r3qJMCpz7xhbuvhBzH7lvPqmSmWZWMglvlgPtVMwu2K2++7kccJ1VBeGPI5vyZYrsu/ts67VWPugEAIIEInBJA3ZjUnWnbGJECDRgj4qcDxGqV0q1apeizFo6dsSTq9f/xz7/FdoUsqeXNG3PmD7BcYBUSdBVO0/+8kEDMP61wvjcvT0Favi94WuF6g9dXOC1ofd5zg+fledPc44L7Rabl54dNbKTldNvRdPgzF/3lbqWRmZ5aYmBVcD/VH4RVzUi3KpmFjwOW9c8PE7S5JCdlGGPIAAAoewicEkQHP11bN3Dpsj+ePsu6HdGRM8plpCtkSS1vBV3xLKnkK5jygr/CYMofYBUGbauzsm3thq22YNUGe2rabxHX2bllXatVJdO0OSsu+3drnmuxUiuh/uuxWgq9+4GtWVtz891t/b/xy7ij1rTAQMofoBUJ0rYFcVUy0wsCtmKWdQFawOMilwsoAclNdj5a9AAApUXglED6ke7YrI799bPP/edHu2J2hUw0ZS2slKoD/eKX0Tiw0iQ3mfbLn6Vq0csvbN36Nyiw2nZ/c8hjfxBWeD9wetBz3eOCbp2bt+aal5BSLW4btuS6W7woZvK3dBUGWUEBWuE0Pf55ZZZ9u2xdVOsNDKouP7K5Xd9lb5cghf1H9GjRAwBsDwInJB26QsavRa+kFhFPaItItMGba63JjF9znrpaKslI9tbgAC0wCNus+yHBWFAAF2G+WtH0GgWvZy7g0y1envlskbt5rWhq5dLlAdz/9FTX7dGl/HfTA+5HmKd1eNksQ6cXeb5bJtUtXx4yTJIVEgCwvQickJToChm/Fr39G9WyPXetWiQwLetn7HVQXxAExDfJiXe5ABeQhQRpBS1f2wIvLwj7a+MW+3vzVlvy5yb7YXlWxNdQpkS1ngVe082NW9vJlwoItS2oKj4g2xZ0lRC4hQRlgctlpoWfXlKrG1khAQCxQOCEpEVXyPghMC3etssFRB+cKbnJCzN/j3p5L8PjtUfvZVcd3cJlPSy4GHZBJsqt3v2Q6e5xXkHaf+/i2aHLFTy38BZmObWoBU4PtG1a/LpAlkStbkFBWWGAtW5zjn8MWWmyQp57cGPXHbK6S1ST7rpalodWNQBAfBA4AdguBKZlI7lJ1Uwd0FtCeN0fvaAsNKgKDbz8QVkxgZs/4ItmXuE61NIW2upW0D1yx5OLjPtqmbt5tIlXy0y36pULAikFVAVBVZpVr5Rh1fW/yLx0q1H4390vnF81I811UU1WJN8AkIwInAAgwcprcpPA7o+WoB5tSr3vLpLtD6gCA6yCwMtlhdy4xX5bu8nGBwRCxWm8SxX330tYsnFLrhuzphgtVklF1HBVLdMLugoDLQVVhYGZF3T55wU+dvMLgjU9X88pS0EYyTfij8AUSAwCJwAoQ0huUjrp7iLNqSW2uqkrZGmyQi4Luc6bWtZcAJWd64KoTVvybMOWHPd/45Yc27glzzZtyfXPL1gm5H7APAVgCsS8x6stum6EJamWmVYQWBUGXaGBl7+1q3A5r+UraNnCwK20B+Ck09+5CEyBxCFwAoAyhjFkZSsrpFrWCrpEplu9HSyLgjBlXwwMqjZkF/zftHXb/aIBmAI3L1jbNs/rqqikILpFO5arJBrLFdzFMExXxIAui1MXrLEPflgV1boD6740FyCvyAhMgbKDwAkAyiDGkCVnVkgFYV7q/d1KcQHu4oIwdSd0gZS/NWxb0LXtcUFrWfiAbNtjL+mIl0Z/bQyCsHAqu+yIqfbSl7/buK+WWmpKirtpG9dNm3rB/23T1RUxrXC66jAtyukF/wvX5dZvBY+96YWvU9L0wLJte43I04Nfu2D9YacHvGa46c9OX2TPTl8cVd0SmALxReAEAKhQkqVFTwfruoCybnWr71gQJhoTVhBgbeuK6FrEwnQ3DA3C1FK2ZkO2/b0pclIOtbjphvhoVLuy7VI102Yv/tuufGmO/0LbBRfdTi1yUW7/48JpgfO9++Xtu7GjGEOG4hA4AQAqHFr0inKJPqqn2a7Vt68r2fd/rLNb354Xcfn+Xfa2vepVt9pVM6xWlUzL9/n81yXTffc/32d5hdPdfe9x4X891jix0Onesu55hcttm14wLdJ0//zC6d60SNPz881ftm3TC8sfRdmLm65ppbF8Xba7xZLS+nuBVLjAquB+QVBWJTM9bJDmf27AfQVu3mNdxiHRXSG97pDPTF9UmN20YAyZsplefkRzfzdIoStkxUTgBAAAttsrs5aWKvnGo58ULEtXsuisXv+vrcoqCEzv+O+PEZfvfWgTa1i7irumWUZ6qrvQtnfhbXdx7sCLcIc+1rKF0wKv0+ZdBmBdDNL8l3QdttDAquC+kr+kBwRhhQFayLJey1pQK1rIfAWAoddii2b7VRAVOraM7bdiInACAAAJS76Bkr06e1mpAtMXvvw9Jgf2avHKzt0WWBUEWQUp+t1ta8G0YoOygPvesv8WLh+4rHcpNiU6iVW6/+IoZqqcHhBQZaa5gG2/3WtaZlqK/bhig7u8QXGUgfKKzs3dOurVrGSzFv3l7y6rsXvuf8DFt7lgdvJ1hSRwAgAASZF8IxklKjBVsgovm2S8L6KdHRKQBQZim0sIwoKW9ZYPma/xdF4wpG6P3nq2h8b0PfzxL1Etq5hJwZMXTPkDq5AgK3C6Aq6CaYHTt01z8wO6SwbNS08tU9dzS9Z0+gROAAAgZpIl+UZZkcyBaeBFtGtZRlwvlJ2tC1qHCbq8/18s/NN124tk73rV3XXQXJKT3IKgTuvWerMDWtAUpG1LhBK/Lo6B1D2zsheshWkF808vXMZ1b0xPtUphArHAwK1SkWlp7nm6hl5FS6dP4AQAAGKK5BvxQ2BaejrAr65bpfRiD+zX7V4zqnX1ObypHbhH7bAH9mpBU1p/F1Dl5NkWFzgVtHp50wqCqcL7GjsW0GKWHbL8lsBpucHz3bzcPP9lBGRrbr67ZWXHr7tjuHFplYppIdP93//abL+u2WjJkk6fwAkAAKAcITBNXHITL3NkuAN7taBlpuuWajUrx68FLbQ1TYk7vEDMa1XT5QWCArGQ4Ksg8AoI4vzLqOtkXrGB3taApCHxGJd272n7+wPTsojACQAAABVSeU9uotY03aqFaU2Lh/z8ggtvF20FK9pS5o0/0/3vlq2ziT+uirh+vQ91SS2rCJwAAABQISXzGLJ4SFX3PGUlzEyLanmvK2StKulRBU66oPa85esZ4wQAAACUZYwhK7tdIcsCAicAAACgEGPIYuf8ct4VMhSBEwAAAICYq5dkXSEJnAAAAADE3XHlvCskgRMAAACAnSKtHHeFjHzJXwAAAACo4AicAAAAACACAicAAAAAiIDACQAAAAAiIHACAAAAgAgInAAAAAAgAgInAAAAAIiAwAkAAAAAIiBwAgAAAIAICJwAAAAAIAICJwAAAACIgMAJAAAAACIgcAIAAACACAicAAAAACACAicAAAAAiIDACQAAAAAiIHACAAAAgAgInAAAAAAgAgInAAAAAIiAwAkAAAAAIiBwAgAAAIAICJwAAAAAIAICJwAAAACIgMAJAAAAACIgcAIAAACACAicAAAAACACAicAAAAAiIDACQAAAAAiIHACAAAAgAgInAAAAAAgAgInAAAAAIiAwAkAAAAAynrg9MQTT1jTpk2tcuXK1rFjR5s9e3aJyw8fPtz22Wcfq1KlijVu3Nj69+9v2dnZO628AAAAACqehAZO48ePtwEDBtiQIUNs7ty51qZNG+vevbutWbMm7PKvvvqq3XLLLW75n3/+2Z5//nm3jltvvXWnlx0AAABAxZHQwOmRRx6xyy67zPr06WOtW7e2UaNGWdWqVW306NFhl58xY4Ydfvjhdt5557lWqm7dulnPnj0jtlIBAAAAwI5ItwTZunWrzZkzxwYNGuSflpqaal26dLGZM2eGfc5hhx1mL7/8sguUDjnkEFu0aJF98MEHduGFFxb7Olu2bHE3T1ZWlvufk5PjbonmlaEslCUZUb/xRf3GF/UbX9RvfFG/8UX9xhf1W3HqN6cUZUjx+Xw+S4AVK1ZYo0aNXCtSp06d/NMHDhxo06ZNs1mzZoV93mOPPWY33nijqdi5ubl25ZVX2lNPPVXs69x55502dOjQsN3+1LoFAAAAoGLavHmz6822fv16q1mzZtlscdoeU6dOtXvvvdeefPJJl0hi4cKF1q9fPxs2bJjdcccdYZ+jFi2NowpscVJSCXXzi1Q5OyvKnTRpknXt2tUyMjISXZykQ/3GF/UbX9RvfFG/8UX9xhf1G1/Ub8Wp36zC3mjRSFjgVLduXUtLS7PVq1cHTdfjBg0ahH2OgiN1y7v00kvd4wMOOMA2bdpkl19+ud12222uq1+oSpUquVsofUiJ/qDKcnmSDfUbX9RvfFG/8UX9xhf1G1/Ub3xRv8lfvxmleP2EJYfIzMy09u3b2+TJk/3T8vPz3ePArnuhTWmhwZGCL0lQj0MAAAAAFUBCu+qpC13v3r2tQ4cOLtmDrtGkFiRl2ZNevXq5cVD33Xefe3zyySe7THzt2rXzd9VTK5SmewEUAAAAACRV4HTOOefY2rVrbfDgwbZq1Spr27atTZw40erXr+/mL126NKiF6fbbb7eUlBT3f/ny5bbbbru5oOmee+5J4LsAAAAAkOwSnhyib9++7lZcMohA6enp7uK3ugEAAABAhbgALgAAAACUBwROAAAAABABgRMAAAAAREDgBAAAAAAREDgBAAAAQAQETgAAAAAQAYETAAAAAERA4AQAAAAAERA4AQAAAEAEBE4AAAAAEAGBEwAAAABEQOAEAAAAABEQOAEAAABABAROAAAAABABgRMAAAAAREDgBAAAAAAREDgBAAAAQAQETgAAAAAQAYETAAAAAERA4AQAAAAAERA4AQAAAEAEBE4AAAAAEAGBEwAAAABEQOAEAAAAABEQOAEAAABABAROAAAAABABgRMAAAAAREDgBAAAAAAREDgBAAAAQAQETgAAAAAQAYETAAAAAERA4AQAAAAAERA4AQAAAEAEBE4AAAAAEAGBEwAAAABEQOAEAAAAALEOnF5++WXbtGlTaZ8GAAAAABUncOrfv7/Vr1/fzjvvPPvggw8sLy8vPiUDAAAAgPIaOK1cudLGjRtnKSkpdvbZZ1vDhg3tmmuusRkzZsSnhAAAAABQ3gKn9PR0O+mkk+yVV16xNWvW2KOPPmpLliyxo48+2lq0aBGfUgIAAABAAqXvyJOrVq1q3bt3t3/++cd+//13+/nnn2NXMgAAAAAoz1n1Nm/e7FqcTjjhBGvUqJENHz7cTjvtNPvxxx9jX0IAAAAAKG8tTueee669//77rrVJY5zuuOMO69SpU3xKBwAAAADlMXBKS0uzCRMmuC56ug8AAAAAya7UgZO66Hmys7OtcuXKsS4TAAAAAJTvMU75+fk2bNgwN7apevXqtmjRIjddXfaef/75eJQRAAAAAMpX4HT33Xfb2LFj7cEHH7TMzEz/9P3339+ee+65WJcPAAAAAMpf4PTiiy/aM888Y+eff37QGKc2bdrY/PnzY10+AAAAACh/gdPy5cttr732CtuFLycnJ1blAgAAAIDyGzi1bt3apk+fXmT6G2+8Ye3atYtVuQAAAACg/GbVGzx4sPXu3du1PKmV6a233rIFCxa4Lny6vhMAAAAAWEVvcTr11FPtvffes08++cSqVavmAqmff/7ZTevatWt8SgkAAAAA5anFSY444gibNGlS7EsDAAAAAMkQOPl8PpszZ44tWbLEUlJSrHnz5ta2bVt3HwAAAACsogdOU6ZMsUsuucR+//13F0CJAqZmzZrZ6NGj7cgjj4xXOQEAAACg7I9xWrhwoZ100knWtGlTlxBC45p++ukne/31122PPfawE044wRYtWhTf0gIAAABAWW5xGj58uB166KE2efLkoOmtWrWy0047zbp06WKPPvqoPf744/EoJwAAAACU/RanqVOn2vXXXx92nrrraZ668gEAAABAhQ2cli5dagcccECx8/fff3839gkAAAAAKmzgtHHjRqtatWqx8zVv8+bNsSoXAAAAAJTPrHpKBrFq1aqw8/78889YlQkAAAAAym/gdOyxx/rTkIeOcdJ0ruUEAAAAoEIHTosXL45vSQAAAACgvAdOTZo0iW9JAAAAAKC8J4cAAAAAgIqKwAkAAAAAynrg9MQTT1jTpk2tcuXK1rFjR5s9e3aJy69bt86uueYaa9iwoVWqVMlatmxpH3zwwU4rLwAAAICKp1RZ9WJt/PjxNmDAABs1apQLmoYPH27du3e3BQsWWL169Yosv3XrVuvataub98Ybb1ijRo3cRXdr166dkPIDAAAAqBgSGjg98sgjdtlll1mfPn3cYwVQ//vf/2z06NF2yy23FFle0//++2+bMWOGZWRkuGlqrQIAAACAhAdO7dq1i/oaTXPnzo1qObUezZkzxwYNGuSflpqaal26dLGZM2eGfc67775rnTp1cl31/vvf/9puu+1m5513nt18882WlpYW9jlbtmxxN09WVpb7n5OT426J5pWhLJQlGVG/8UX9xhf1G1/Ub3xRv/FF/cYX9Vtx6jenFGWIKnDq0aOH/352drY9+eST1rp1axfEyJdffmk//vijXX311VG/8J9//ml5eXlWv379oOl6PH/+/LDPWbRokX366ad2/vnnu3FNCxcudK+pNzxkyJCwz7nvvvts6NChRaZ//PHHVrVqVSsrJk2alOgiJDXqN76o3/iifuOL+o0v6je+qN/4on6Tv343b94c9bIpPp/PV5qVX3rppS4xw7Bhw4KmK3BZtmyZ604XjRUrVrgxSup25wVgMnDgQJs2bZrNmjWryHOUCEKBmy7G67UwqbvfQw89ZCtXroy6xalx48YucKtZs6YlmoI+bTQau+V1P0TsUL/xRf3GF/UbX9RvfFG/8UX9xhf1W3HqNysry+rWrWvr16+PGBuUeozT66+/bl9//XWR6RdccIF16NAh6sBJBVTws3r16qDpetygQYOwz1HApsoN7Ja377772qpVq1zXv8zMzCLPUeY93UJpPYn+oMpyeZIN9Rtf1G98Ub/xRf3GF/UbX9RvfFG/yV+/GaV4/VKnI69SpYp98cUXRaZrmlKKR0tBTvv27W3y5Mn+afn5+e5xYAtUoMMPP9x1z9Nynl9++cUFVOGCJgAAAACIhVK3OF1//fV21VVXuSQQhxxyiJumbnVqabrjjjtKtS6lIu/du7drqdK6lI5806ZN/ix7vXr1ct35NE5J9LojR460fv362bXXXmu//vqr3XvvvXbdddeV9m0AAAAAQPwCJ6UJb968uY0YMcJefvllf3e5MWPG2Nlnn12qdZ1zzjm2du1aGzx4sOtu17ZtW5s4caI/YcTSpUtdpj2PxiZ99NFH1r9/fzvwwANdUKUgSln1AAAAAKBMXcdJAVJpg6Ti9O3b193CmTp1apFp6sanLH4AAAAAsLOUeoyTWpv++uuvItPXrVvn5gEAAACAVfTAacmSJe76S6GU8nv58uWxKhcAAAAAlL+ueu+++67/vsYZ1apVy/9YgZSy4TVt2jT2JQQAAACA8hI49ejRw/1PSUlxmfBC858raHr44YdjX0IAAAAAKC+Bk3ftpGbNmtlXX33lLmALAAAAABVBqbPqLV68OD4lAQAAAIDynhxi5syZ9v777wdNe/HFF10LVL169ezyyy93CSIAAAAAoMIGTnfddZf9+OOP/sc//PCDXXLJJdalSxd3Udz33nvP7rvvvniVEwAAAADKfuD07bff2rHHHut/PG7cOOvYsaM9++yzNmDAAHvsscdswoQJ8SonAAAAAJT9wOmff/6x+vXr+x9PmzbNjj/+eP/jgw8+2JYtWxb7EgIAAABAeQmcFDR5iSG2bt1qc+fOtUMPPdQ/f8OGDS4tOQAAAABU2MDphBNOcGOZpk+fboMGDbKqVavaEUcc4Z///fffW4sWLeJVTgAAAAAo++nIhw0bZqeffrp17tzZqlevbi+88IJlZmb6548ePdq6desWr3ICAAAAQNkPnHTB288++8zWr1/vAqe0tLSg+a+//rqbDgAAAABW0S+AW6tWrbDT69SpE4vyAAAAAED5HeMEAAAAABUVgRMAAAAAREDgBAAAAAAREDgBAAAAQLwCpz/++MPy8/P9j3V/6dKl27s6AAAAAEi+wKl169a2ZMkS/+O1a9das2bNYlUuAAAAACj/gZPP54tqGgAAAACUdzEd45SSkhLL1QEAAABA+bsA7osvvui/n5uba2+99ZbVq1fPPe7evXvsSwcAAAAA5S1wGjNmjP9+Tk6OvfHGG1alShXX0kTgBAAAACBZlSpwmjJliv9+jRo17NVXX7XmzZu7x6tXr4596QAAAACgDOA6TgAAAACwMwMnsuoBAAAASEbbHTjdeuutVqdOHf/j6tWr25AhQ2JVLgAAAAAon2OcAg0aNCjocbVq1QicAAAAACQlxjgBAAAAQAQETgAAAAAQAYETAAAAAERA4AQAAAAAERA4AQAAAECsAqc5c+bY0UcfbVlZWUXmrV+/3s377rvvol0dAAAAACRf4PTwww/bMcccYzVr1iwyr1atWta1a1d76KGHYl0+AAAAACg/gdOsWbPs1FNPLXb+ySefbDNmzIhVuQAAAACg/AVOy5cvtxo1ahQ7v3r16rZy5cpYlQsAAAAAyl/gtNtuu9mCBQuKnT9//nyrW7durMoFAAAAAOUvcOrSpYvdc889Yef5fD43T8sAAAAAQLJJj3bB22+/3dq3b28dO3a0G264wfbZZx9/S5MSR/zyyy82duzYeJYVAAAAAMp24NSiRQv75JNP7KKLLrJzzz3XUlJS/K1NrVu3tkmTJtlee+0Vz7ICAAAAQNkOnKRDhw42b948++abb2zhwoUuaGrZsqW1bds2fiUEAAAAgPIUOHnatWvnbgAAAABQEUQdOJ1++ulhp+vit2p1uvTSS13mPQAAAACosFn1FCCFu61bt86effZZlyxC3fgAAAAAoMK2OI0ZM6bYefn5+XbZZZfZoEGD7L333otV2QAAAACgfLU4lbiS1FS77rrrbM6cObFYHQAAAAAkX+Ak1apVs82bN8dqdQAAAACQfIGTruOkJBEAAAAAUGHHOL377rthp69fv9510XvuuefcDQAAAAAqbODUo0ePsNNr1KjhMuopaDr33HNjWTYAAAAAKF+BkzLnAQAAAEBFFLMxTrqe08iRI2O1OgAAAABInsBp8uTJdt5551nDhg1tyJAhsSkVAAAAAJT3wGnZsmV21113WbNmzaxbt26WkpJib7/9tq1atSr2JQQAAACA8hI45eTk2Ouvv27du3d3ySC+/fZbe+ihh9zFb2+77TY77rjjLCMjI76lBQAAAICynByiUaNG1qpVK7vgggts3Lhxtssuu7jpPXv2jGf5AAAAAKD8tDjl5ua6Lnm6paWlxbdUAAAAAFAeA6cVK1bY5Zdfbq+99po1aNDAzjjjDDeuSYEUAAAAACSzqAOnypUr2/nnn2+ffvqp/fDDD7bvvvvadddd51qi7rnnHps0aZLl5eXFt7QAAAAAUF6y6rVo0cLuvvtu+/333+1///ufbdmyxU466SSrX79+7EsIAAAAAOUlOUQ4yqh3/PHHu9vatWvtpZdeil3JAAAAACBZLoDr2W233WzAgAGxWh0AAAAAJF/gBAAAAADJqkwETk888YQ1bdrUJaDo2LGjzZ49O6rn6XpSyurXo0ePuJcRAAAAQMWV8MBp/PjxrovfkCFDbO7cudamTRvr3r27rVmzpsTnLVmyxG688UY74ogjdlpZAQAAAFRMCQ+cHnnkEbvsssusT58+1rp1axs1apRVrVrVRo8eXexzlPZcqdGHDh1qzZs336nlBQAAAFDxlDqrnoKWsWPH2uTJk12rUH5+ftB8XecpWlu3brU5c+bYoEGDgjL1denSxWbOnFns8+666y6rV6+eXXLJJTZ9+vQSX0Op0nXzZGVluf85OTnulmheGcpCWZIR9Rtf1G98Ub/xRf3GF/UbX9RvfFG/Fad+c0pRhlIHTv369XOB04knnmj777+/G2O0vf78808XiIVe/0mP58+fH/Y5n3/+uT3//PP27bffRvUa9913n2uZCvXxxx+7lq2yQhcQRvxQv/FF/cYX9Rtf1G98Ub/xRf3GF/Wb/PW7efPm+AVOSsgwYcIEO+GEE2xn27Bhg1144YX27LPPWt26daN6jlqzAtOkq8WpcePG1q1bN6tZs6aVhShXG03Xrl0tIyMj0cVJOtRvfFG/8UX9xhf1G1/Ub3xRv/FF/Vac+s0q7I0Wl8ApMzPT9tprL4sFBT9paWm2evXqoOl63KBBgyLL//bbby4pxMknn+yf5nUVTE9PtwULFliLFi2CnlOpUiV3C6UPKdEfVFkuT7KhfuOL+o0v6je+qN/4on7ji/qNL+o3+es3oxSvX+rkEDfccIONGDHCfD6f7SgFYe3bt3fjpQIDIT3u1KlTkeVbtWplP/zwg+um591OOeUUO/roo919tSQBAAAAQKyVusVJY4ymTJliH374oe23335ForS33nqrVOtTN7revXtbhw4d7JBDDrHhw4fbpk2bXJY96dWrlzVq1MiNVdJ1njSuKlDt2rXd/9DpAAAAAJCwwEmBymmnnRazApxzzjm2du1aGzx4sK1atcratm1rEydO9CeMWLp0qcu0BwAAAADlJnAaM2ZMzAvRt29fdwtn6tSpJT5XGf4AAAAAIJ5oygEAAACAWLc4yRtvvOFSkqsbnS5iG2ju3LmxKhsAAAAAlM8Wp8cee8wlbtAYpG+++cYldNh1111t0aJFdvzxx8enlAAAAABQngKnJ5980p555hl7/PHHXTrxgQMHugtYXXfddbZ+/fr4lBIAAAAAylPgpO55hx12mLtfpUoV27Bhg7t/4YUX2muvvRb7EgIAAABAeQucGjRoYH///be7v+eee9qXX37p7i9evDgmF8UFAAAAgHIfOB1zzDH27rvvuvsa69S/f3/r2rWrux5TLK/vBAAAAADlNquexjfl5+e7+9dcc41LDDFjxgw75ZRT7IorrohHGQEAAACgfAVOqamp7uY599xz3Q0AAAAAktV2XQB3+vTpdsEFF1inTp1s+fLlbtpLL71kn3/+eazLBwAAAADlL3B68803rXv37i6jnq7jtGXLFjddqcjvvffeeJQRAAAAAMpX4HT33XfbqFGj7Nlnn7WMjAz/9MMPP9zmzp0b6/IBAAAAQPkLnBYsWGBHHnlkkem1atWydevWxapcAAAAAFC+r+O0cOHCItM1vql58+axKhcAAAAAlN/A6bLLLrN+/frZrFmzLCUlxVasWGGvvPKK3XjjjXbVVVfFp5QAAAAAUJ7Skd9yyy3uOk7HHnusbd682XXbq1Spkgucrr322viUEgAAAADKU+CkVqbbbrvNbrrpJtdlb+PGjda6dWurXr16fEoIAAAAAOUtcPJkZma6gAkAAAAAkl3UgdPFF18c1XKjR4/ekfIAAAAAQPkNnMaOHWtNmjSxdu3amc/ni2+pAAAAAKA8Bk7KmPfaa6/Z4sWLrU+fPnbBBRdYnTp14ls6AAAAAChP6cifeOIJW7lypQ0cONDee+89a9y4sZ199tn20Ucf0QIFAAAAIKmV6jpOSjves2dPmzRpkv3000+233772dVXX21NmzZ12fUAAAAAIBmlbvcTU1NdanK1NuXl5cW2VAAAAABQXgOnLVu2uHFOXbt2tZYtW9oPP/xgI0eOtKVLl3IdJwAAAABJK+rkEOqSN27cODe2SanJFUDVrVs3vqUDAAAAgPIUOI0aNcr23HNPa968uU2bNs3dwnnrrbdiWT4AAAAAKD+BU69evdyYJgAAAACoaEp1AVwAAAAAqIi2O6seAAAAAFQUBE4AAAAAEAGBEwAAAABEQOAEAAAAABEQOAEAAABABAROAAAAABABgRMAAAAAREDgBAAAAAAREDgBAAAAQAQETgAAAAAQAYETAAAAAERA4AQAAAAAERA4AQAAAEAEBE4AAAAAEAGBEwAAAABEQOAEAAAAABEQOAEAAABABAROAAAAABABgRMAAAAAREDgBAAAAAAREDgBAAAAQAQETgAAAAAQAYETAAAAAERA4AQAAAAAERA4AQAAAEAEBE4AAAAAEAGBEwAAAABEQOAEAAAAABEQOAEAAABABAROAAAAABABgRMAAAAAREDgBAAAAAAREDgBAAAAQAQETgAAAAAQAYETAAAAAERA4AQAAAAAERA4AQAAAEB5CJyeeOIJa9q0qVWuXNk6duxos2fPLnbZZ5991o444gjbZZdd3K1Lly4lLg8AAAAA5T5wGj9+vA0YMMCGDBlic+fOtTZt2lj37t1tzZo1YZefOnWq9ezZ06ZMmWIzZ860xo0bW7du3Wz58uU7vewAAAAAKoaEB06PPPKIXXbZZdanTx9r3bq1jRo1yqpWrWqjR48Ou/wrr7xiV199tbVt29ZatWplzz33nOXn59vkyZN3etkBAAAAVAzpiXzxrVu32pw5c2zQoEH+aampqa77nVqTorF582bLycmxOnXqhJ2/ZcsWd/NkZWW5/3qObonmlaEslCUZUb/xRf3GF/UbX9RvfFG/8UX9xhf1W3HqN6cUZUjx+Xw+S5AVK1ZYo0aNbMaMGdapUyf/9IEDB9q0adNs1qxZEdeh1qePPvrIfvzxRzdGKtSdd95pQ4cOLTL91VdfdS1bAAAAACqmzZs323nnnWfr16+3mjVrlt0Wpx11//3327hx49y4p3BBk6g1S2OoAlucvHFRkSpnZ0W5kyZNsq5du1pGRkaii5N0qN/4on7ji/qNL+o3vqjf+KJ+44v6rTj1m1XYGy0aCQ2c6tata2lpabZ69eqg6XrcoEGDEp/7n//8xwVOn3zyiR144IHFLlepUiV3C6UPKdEfVFkuT7KhfuOL+o0v6je+qN/4on7ji/qNL+o3+es3oxSvn9DkEJmZmda+ffugxA5eoofArnuhHnzwQRs2bJhNnDjROnTosJNKCwAAAKCiSnhXPXWj6927twuADjnkEBs+fLht2rTJZdmTXr16uXFQ9913n3v8wAMP2ODBg90YJV37adWqVW569erV3Q0AAAAAki5wOuecc2zt2rUuGFIQpDTjakmqX7++m7906VKXac/z1FNPuWx8Z555ZtB6dB0oJYIAAAAAgKQLnKRv377uFo4SPwRasmTJTioVAAAAAJSRC+ACAAAAQFlH4AQAAAAAERA4AQAAAEAEBE4AAAAAEAGBEwAAAABEQOAEAAAAABEQOAEAAABABAROAAAAABABgRMAAAAAREDgBAAAAAAREDgBAAAAQAQETgAAAAAQAYETAAAAAERA4AQAAAAAERA4AQAAAEAEBE4AAAAAEEF6pAUqqry8PMvJyYn76+g10tPTLTs7270mElu/GRkZlpaWtlPKBgAAgPKDwCmEz+ezVatW2bp163ba6zVo0MCWLVtmKSkpO+U1K5Ltqd/atWu75/B5AAAAwEPgFMILmurVq2dVq1aN+8Fzfn6+bdy40apXr26pqfScTGT9KsjavHmzrVmzxj1u2LDhTiolAAAAyjoCpwDqyuUFTbvuuutOO7DfunWrVa5cmcCpDNRvlSpV3H8FT9oO6LYHAAAA4Ug9gDemSS1NqLi8z39njHEDAABA+UDgFAZjWyo2Pn8AAACEInACAAAAgAgInJA0rUTvvPNO1MtfdNFF1qNHj7iWCQAAAMmDwClJKBBQ8ODdlNziuOOOs++//z6h5Ro7dqwrz7777ltk3uuvv+7mNW3aNCFlAwAAAKJF4JREFCitXLnS3SZPnuwu/HrSSSclulhWrVo1l6Vu5syZQdOff/5523PPPRNWLgAAACBaBE7R2rSp+Ft2dvTL/vtvdMtuh0qVKrkLt+rWtm1bu+WWW9yFX9euXetf5uabb7aWLVu6zHHNmze3O+64Iyh73HfffWdHH3201ahRw2rWrGnt27e3r7/+2j//888/tyOOOMKl7W7cuLFdd911tilCeRXAnXfeeTZ69Gj/tD/++MOmTp3qpod66qmnrEWLFpaZmWn77LOPvfTSS0Hzf/31VzvyyCNdivHWrVvbpEmTiqxD7/vss8+2OnXqWLNmzVy3vCVLlpSiNgEAAIBtCJyiVb168bczzghetl694pc9/vigRVOaN7fae+xhqTVrBi+3g3TR15dfftn22muvoGtSKSBS97mffvrJRowYYc8++6w9+uij/vnnn3++7bHHHvbVV1/ZnDlzXPCVkZHh5v3222+uVeuMM85wXQDHjx/vAqm+fftGLM/FF19sEyZMcBeYFZVB66pfv37Qcm+//bb169fPbrjhBps3b55dccUV1qdPH5syZYr/ukynn366C6pmzZplo0aNcsFgIAWC3bt3d+912rRpNnHiRHcBXL2erukEAAAAlBYXwE0i77//vgsQRK1ADRs2dNMCL/x6++23++9rbNGNN95o48aNs4EDB7ppS5cutZtuuslatWrlHu+9997+5e+77z4XWF1//fX+eY899ph17tzZtRKpBag47dq1cy1cb7zxhl144YUucHrkkUds0aJFQcv95z//ceO1rr76avd4wIAB9uWXX7rpagn75JNPbP78+fbRRx/Z7rvv7pa599577fiAgFQBnQKs5557znw+n2VlZbnWLrU+qZWrW7duO1jTAAAAqGgInKK1cWPx89LSgh+vWVP8sgFBjPgWLbL1WVmuW1xggLM9FFgogJF//vnHnnzySRdQzJ4925o0aeIPKhTsqPVIrVK5ubnutT0KVC699FLXPa5Lly521llnuW5zXjc+tTS98sor28rv87kgZfHixWETQIS2Oo0ZM8aNa1Jgd8IJJ9jIkSODlvn555/t8ssvD5p2+OGHu9Yxb766CHpBk3Tq1CloeZVz4cKFrsUpUHZ2tnvfAAAAQGkROEWrWrX4LZuXV/B/BwMnJWFQ1zyPWlxq1arluuPdfffdLjmDWoyGDh3qurJpnlqbHn74Yf9z7rzzTjfu6H//+599+OGHNmTIELfMaaed5gItdZ3TuKZQ0SR50GurZUuvoVYnjX2KB5VTY7MU4Cmo02O1xCkw3W233eLymgAAAEhuBE5JTKm+FSz8W5iQYsaMGa7l6bbbbvMv8/vvvxd5npJH6Na/f3/r2bOnayVS4HTQQQe5sVGBwVlpqKvcKaec4sY6aWxSOGq1+uKLL6x3797+aXqsJBDefCV+UOZAdUUUdeULpHKqZa1evXouYMqKUYseAAAAKi6OJJPIli1bbNWqVe6mLm3XXnuta205+eST/WOSNIZJLUjqsqYue0rG4FGApUQPGgekgEoBi5JEeF3wlIRBwZeW+fbbb112u//+979RJYfwaGzTn3/+6R9DFUrjq7SMuhxq/RoH9dZbb7mxWKLugwrqFFipS9706dODAkGvZatu3bp26qmnuvl6L3pPailTNj8AAACgtAickoiyx6kVRreOHTu6oEcXmT3qqKPcfLX2qBVJgY7SlSsIUjpyT1pamv3111/Wq1cvF5wonbfGSKlrnxx44IEuS90vv/ziUpIr4cPgwYODxhtFojTmgVn+QiltuMYzKRnEfvvtZ08//bRr8fLeg1qNFOwpyDvkkEPceKx77rknaB1Ktf7ZZ5+57oNnnnmmq4vLLrvMjXEKHM8FAAAARCvFp9H9FYi6bWlsz/r164scROvAWkkOdN2fkjLExZLG4NCVrGzVbyK2g/JKqd8/+OADl+jDS1uP2KF+44v6jS/qN76o3/iifitO/WaVEBuE4kgdAAAAACIgcAIAAACACAicAAAAACACAicAAAAAiIDACQAAAAAiIHACAAAAgAjSIy2AyNZkZduaDVtK/bx6NSpZ3eqZcSkTAAAAgNghcIqBV2YttRGTfy318/odu7f1O3avuJQJAAAAQOwQOMXA+R33tK6t6wdNy87JszNHzXT337iyk1XOSAvb4gQAAACg7CNwioF6NSu7W6AN2Tn++xu35Fq7PXextNSUIs/Nz8/fKWUEAAAAsP1IDhEHE+ettC6PTPM/vmjMV/Z/D3zqpsfbzJkzLS0tzU488cSg6VOnTrWUlBRbt25dkec0bdrUhg8fHjRtypQpdsIJJ9iuu+5qVatWtdatW9sNN9xgy5cvj/t7AAAAAMoaAqcYU3B01ctzbXVWcLKIVeuz3fR4B0/PP/+8XXvttfbZZ5/ZihUrtmsdTz/9tHXp0sUaNGhgb775pv300082atQoW79+vT388MMxLzMAAABQ1tFVLwKfz2f/5uRFtWxevs+GvPuj+cKtx8zUUe/Od3+yw/eq6++2p656/27Ns/StuZaaGhzHVslIc61E0dq4caONHz/evv76a1u1apWNHTvWbr31ViuNP/74w6677jp3e/TRR4NapY488siwLVYAAABAsiNwikBBU+vBH8VkXQqeVmVl2wF3fhzV8j/d1d2qZkb/EU2YMMFatWpl++yzj11wwQV2/fXX26BBg0oVfL3++uu2detWGzhwYNj5tWvXjnpdAAAAQLKgq14SUTc9BUxy3HHHua5106ZtG2sVjV9//dVq1qxpDRs2jFMpAQAAgPKHFqcI1F1OLT/RmL34b5cIIpKxfQ62Q5rV8XfV25C1wWrUrBG2q160FixYYLNnz7a3337bPU5PT7dzzjnHBVNHHXVUqbomlqaFCgAAAKgICJwiUBARbXe5I/bezRrWquwSQYQb56RwpEGtym65wDFOuZlp7jVCA6fSUICUm5tru+++e1AQVKlSJRs5cqRrRRK1QoV2t9O4pVq1arn7LVu2dMusXLmSVicAAACgEF31YkjB0JCTW7v7oW023mPND3c9px2hgOnFF190Ge++/fZb/+27775zgdRrr71me++9twvM5syZE/TcRYsWuUBJAZOceeaZlpmZaQ8++GDY1yI5BAAAACoiWpxi7Lj9G9pTFxzksusFpiRXS5OCJs2Ptffff9/++ecfu+SSS/wtR54zzjjDtUZdeeWVdumll7prMakb3wEHHGDLli2zm2++2Q499FA77LDD3PKNGzd22fT69u1rWVlZ1qtXL5dRT9n2FJxVr16dlOQAAACocGhxigMFR58M6Bw0punzm4+JS9AkCox03aXQoMkLnJSe/Pvvv7cRI0ZY7969XbC033772UUXXWQHHnigvffee0Hjmq6++mr7+OOP3cVuTzvtNJepT0GXuvvdeOONcXkPAAAAQFlGi1MMrMnKtjUbgi94mx1w7afqldLt55VZRZ5Xr0Ylq1s9c4dfX4FPcQ455BA31slz5513ulskCsR0AwAAAEDgFBOvzFpqIyb/Wuz8M0fNDDu937F7W79j94pjyQAAAADEAoFTDJzfcU/r2rp+qZ+nFicAAAAAZR+BUwzUq1nZ3baH0pEDAAAAKNtIDgEAAAAAERA4hRGYTAEVD58/AAAAQhE4BcjIyHD/N2/enOiiIIG8z9/bHgAAAADGOAVIS0uz2rVr25o1a9zjqlWrBl3fKB40xmnr1q2WnZ1tqanEsYmsX7U0KWjS56/tQNsDAAAAIAROIRo0aOD+e8FTvOlg/d9//7UqVarEPUiriLanfhU0edsBAAAAIAROIXRw3bBhQ6tXr57l5OTE/fX0Gp999pkdeeSRdA0rA/WrZWhpAgAAQCgCp2Lo4HlnHEDrNXJzc61y5coETnFA/QIAACAWysSgmieeeMKaNm3qDm47duxos2fPLnH5119/3Vq1auWWP+CAA+yDDz7YaWUFAAAAUPEkPHAaP368DRgwwIYMGWJz5861Nm3aWPfu3YsdYzRjxgzr2bOnXXLJJfbNN99Yjx493G3evHk7vewAAAAAKoaEB06PPPKIXXbZZdanTx9r3bq1jRo1ymWzGz16dNjlR4wYYccdd5zddNNNtu+++9qwYcPsoIMOspEjR+70sgMAAACoGBI6xklpoufMmWODBg3yT1PK6C5dutjMmTPDPkfT1UIVSC1U77zzTtjlt2zZ4m6e9evXu/9///33Tkn+EInKoBTYf/31F2Nw4oD6jS/qN76o3/iifuOL+o0v6je+qN+KU78bNmzwZ2Iu04HTn3/+aXl5eVa/fv2g6Xo8f/78sM9ZtWpV2OU1PZz77rvPhg4dWmR6s2bNdqjsAAAAAJKDAqhatWpV7Kx6as0KbKHSBVHV2rTrrruWiesmZWVlWePGjW3ZsmVWs2bNRBcn6VC/8UX9xhf1G1/Ub3xRv/FF/cYX9Vtx6tfn87mgaffdd4+4bEIDp7p167p00atXrw6arsfFXYBU00uzfKVKldwt9AKnZY02mkRvOMmM+o0v6je+qN/4on7ji/qNL+o3vqjfilG/tSK0NJWJ5BCZmZnWvn17mzx5clCLkB536tQp7HM0PXB5mTRpUrHLAwAAAMCOSnhXPXWj6927t3Xo0MEOOeQQGz58uG3atMll2ZNevXpZo0aN3Fgl6devn3Xu3NkefvhhO/HEE23cuHH29ddf2zPPPJPgdwIAAAAgWSU8cDrnnHNs7dq1NnjwYJfgoW3btjZx4kR/AoilS5e6THueww47zF599VW7/fbb7dZbb7W9997bZdTbf//9rTxSN0Jdwyq0OyFig/qNL+o3vqjf+KJ+44v6jS/qN76o3/iqVE7rN8UXTe49AAAAAKjAEn4BXAAAAAAo6wicAAAAACACAicAAAAAiIDACQAAAAAiIHBKAKVWP/jgg61GjRpWr14969Gjhy1YsCDRxUoaTz31lB144IH+i6rpGl8ffvhhoouVtO6//35LSUmx66+/PtFFSQp33nmnq8/AW6tWrRJdrKSyfPlyu+CCC2zXXXe1KlWq2AEHHOAua4HYaNq0aZFtWLdrrrkm0UUr9/Ly8uyOO+6wZs2auW23RYsWNmzYMCPPV+xs2LDB/Z41adLE1bGyOX/11VeJLla59dlnn9nJJ59su+++u9sPKBN2IG27yqzdsGFDV99dunSxX3/91coqAqcEmDZtmvsB+fLLL93Fe3Nycqxbt27u+lXYcXvssYc7mJ8zZ447GDrmmGPs1FNPtR9//DHRRUs6+jF5+umnXaCK2Nlvv/1s5cqV/tvnn3+e6CIljX/++ccOP/xwy8jIcCdUfvrpJ3ddwF122SXRRUuq/ULg9qvfOTnrrLMSXbRy74EHHnAnB0eOHGk///yze/zggw/a448/nuiiJY1LL73UbbMvvfSS/fDDD+74TAfzOuGC0tOxbZs2beyJJ54IO1/b72OPPWajRo2yWbNmWbVq1ax79+6WnZ1tZRHpyMsAXcdKLU8KqI488shEFycp1alTxx566CG75JJLEl2UpLFx40Y76KCD7Mknn7S7777bXYNNF7DGjrc46Yzct99+m+iiJKVbbrnFvvjiC5s+fXqii1Jh6Oz9+++/784i64wztt9JJ53krnP5/PPP+6edccYZ7kz9yy+/nNCyJYN///3X9Qb673//ayeeeKJ/evv27e344493v3XYfvr+v/32266nlSgEUUvUDTfcYDfeeKObtn79ereNjx071s4991wra2hxKgO0kXgH94h9t4Zx48a5Mx7qsofYUaupflh0Jg6xpQNM/Zg0b97czj//fHchcMTGu+++ax06dHCtHzph1a5dO3v22WcTXayktXXrVndAf/HFFxM0xYC6jU2ePNl++eUX9/i7775zLdI6qMeOy83NdccNlStXDpquwJSW/9hbvHixrVq1Kug4olatWtaxY0ebOXOmlUXpiS5ARZefn+/OxqnryP7775/o4iQNNa8rUFJTb/Xq1d0ZjtatWye6WElDwejcuXPp9x0H+sHQmbZ99tnHdXMaOnSoHXHEETZv3jx3JhQ7ZtGiRa6r04ABA+zWW2912/B1111nmZmZ1rt370QXL+mo9XTdunV20UUXJbooSdNimpWV5cY9pqWluYP8e+65x51gwY7TPlbHDho3tu+++7qWj9dee80dxO+1116JLl7SWbVqlfuveg6kx968sobAqQyctdcBEWcyYksHnerqpNa8N954wx0QqSskwdOOW7ZsmfXr18/1AQ89K4cdF3jmWGPHFEhpkPKECRPoahqjk1Vqcbr33nvdY7U4aR+s/vUETrGnLmXaptWCih2n/cArr7xir776qhsLqd85nXxV/bL9xobGNqmFtFGjRi44VZf0nj17unHTAF31Eqhv376u3/eUKVNcQgPEjs4e6+yQ+iUri6EGJo4YMSLRxUoK+vFYs2aN+zFJT093NwWlGtyp+zoDitipXbu2tWzZ0hYuXJjooiQFZW4KPYGiM8t0h4y933//3T755BM32B6xcdNNN7lWJ439UDbICy+80Pr37+9+5xAbylSo3zSN49WJwtmzZ7skXuo6jdhq0KCB+7969eqg6XrszStrCJwSQIPhFDSp+9inn37q0ooi/meZt2zZkuhiJIVjjz3WdYXUmU7vpjP46iqi+zpDh9jRj/dvv/3mDvix49QtOvTyDxovolY9xNaYMWPcOLLAQfbYMZs3b7bU1OBDN+1z9RuH2FJ2N+13lYnzo48+ctl5EVs6/lWApHF7HnVFVXa9sjouna56Ceqep2Z2ZW1Rf1qvH6cGxGkAInbMoEGDXNeQPffc012PQXU9depUt+PDjtM2GzoeTz8wuiYO4/R2nDIL6ZoXOpBfsWKFDRkyxB0YqasIdpzOzmuAvbrqnX322e5s8jPPPONuiB0dyCtwUvcxtUQjNrRv0Jgm/b6pq94333xjjzzyiOtahtjQsYJOcKvLv1r61cqnMWV9+vRJdNHK7cm/hQE9JpQQQidZlRBN27G6mipb4d577+0CKV2nTF1Pvcx7ZY7SkWPnUrWHu40ZMybRRUsKF198sa9Jkya+zMxM32677eY79thjfR9//HGii5XUOnfu7OvXr1+ii5EUzjnnHF/Dhg3d9tuoUSP3eOHChYkuVlJ57733fPvvv7+vUqVKvlatWvmeeeaZRBcp6Xz00Ufud23BggWJLkpSycrKcvvaPffc01e5cmVf8+bNfbfddptvy5YtiS5a0hg/fryrV+2DGzRo4Lvmmmt869atS3Sxyq0pU6aEPebt3bu3m5+fn++74447fPXr13f7ZB2zleX9BtdxAgAAAIAIGOMEAAAAABEQOAEAAABABAROAAAAABABgRMAAAAAREDgBAAAAAAREDgBAAAAQAQETgAAAAAQAYETAAAAAERA4AQAKDOWLFliKSkp9u2331pZMX/+fDv00EOtcuXK1rZt24SUoWnTpjZ8+PCEvDYAoACBEwDA76KLLnKBy/333x80/Z133nHTK6IhQ4ZYtWrVbMGCBTZ58uSwy1BvAJD8CJwAAEHUsvLAAw/YP//8Y8li69at2/3c3377zf7v//7PmjRpYrvuumuFqjcAwDYETgCAIF26dLEGDRrYfffdV+wyd955Z5Fua+pKpi5lga0wPXr0sHvvvdfq169vtWvXtrvuustyc3Ptpptusjp16tgee+xhY8aMCds97rDDDnPByP7772/Tpk0Lmj9v3jw7/vjjrXr16m7dF154of3555/++UcddZT17dvXrr/+eqtbt65179497PvIz893ZVI5KlWq5N7TxIkT/fPVWjRnzhy3jO7rfe9Ivcmbb75p++23n3s91dfDDz8cNH/NmjV28sknW5UqVaxZs2b2yiuvFFnHunXr7NJLL7XddtvNatasacccc4x99913/vm6f/TRR1uNGjXc/Pbt29vXX39dYrkAACUjcAIABElLS3PBzuOPP25//PHHDq3r008/tRUrVthnn31mjzzyiOv2dtJJJ9kuu+xis2bNsiuvvNKuuOKKIq+jwOqGG26wb775xjp16uQCib/++ssfNChQaNeunQsGFOisXr3azj777KB1vPDCC5aZmWlffPGFjRo1Kmz5RowY4QKX//znP/b999+7AOuUU06xX3/91c1fuXKlC3JUFt2/8cYbd6jeFISpnOeee6798MMPLhC74447bOzYsUEB57Jly2zKlCn2xhtv2JNPPumCqUBnnXWWm/bhhx+6dR500EF27LHH2t9//+3mn3/++S4Y/Oqrr9z8W265xTIyMiJ8WgCAEvkAACjUu3dv36mnnuruH3roob6LL77Y3X/77bd9gT8ZQ4YM8bVp0ybouY8++qivSZMmQevS47y8PP+0ffbZx3fEEUf4H+fm5vqqVavme+2119zjxYsXu9e5//77/cvk5OT49thjD98DDzzgHg8bNszXrVu3oNdetmyZe96CBQvc486dO/vatWsX8f3uvvvuvnvuuSdo2sEHH+y7+uqr/Y/1PvV+Y1Fv5513nq9r165Bz73pppt8rVu3dvdVfi0/e/Zs//yff/7ZTVP9yvTp0301a9b0ZWdnB62nRYsWvqefftrdr1Gjhm/s2LER3z8AIHq0OAEAwtJ4HbXa/Pzzz9u9DrXWpKZu+6lRt7oDDjggqJVG44ZCW1TUyuRJT0+3Dh06+MuhbmhqjVE3Pe/WqlUr/3gkj7qnlSQrK8u1hh1++OFB0/V4R95zSfWmaeFeTy1ceXl5br7eb2DZ9d7UzdGj979x40ZXb4F1sHjxYv/7HzBggOvKp+6DSlgRWC8AgO1D4AQACOvII490XdcGDRpUZJ6CIZ9PDSHb5OTkFFkutHuYxgmFm6axRtFS0KCue0pZHnhT8KEye5QJr6zVWyzo/Tds2LDI+1fWP3VxFHUB/PHHH+3EE0903SVbt25tb7/9dlzKAwAVRXqiCwAAKLvUWqGECfvss0/QdCUlWLVqlQuevHTbsbz20pdffukPgpRMQuN0lOxBNJ5HCRaUWEGtM9tLSRN23313Nwaqc+fO/ul6fMghh8Sl3vbdd1+3/kB63LJlS9f6ptYl7/0efPDBbr4CIo3r8uj9q+713gOTcYTSOnXr37+/9ezZ0yXhOO2003bofQFARUaLEwCgWOpWp0QDjz32WNB0Za1bu3atPfjgg64b2BNPPOESFcSK1qcWEmXXu+aaa1yK74svvtjN02MlQVAwoOQHev2PPvrI+vTp47q7lYZaaNS1bvz48S5AURIFBYD9+vWLS70pyYSuBTVs2DD75ZdfXJe+kSNH+pNOKNA67rjjXMIMJc9QAKUud8qw51H3O3VlVMbCjz/+2F00eMaMGXbbbbe5ZBn//vuvCzKnTp1qv//+uwvMVE8K2gAA24/ACQBQIqXiDu1Kp4NwZXtTgNOmTRubPXt2iRnntqfFRjet+/PPP7d3333XpRUXr5VIQVK3bt1ckKK04xoHFDieKhrXXXedGw+kgEbrUYY+vdbee+8dl3pTa9GECRNs3LhxLs364MGD3XLKpOdRy5Deo1rBTj/9dLv88sutXr16/vlq4fvggw9ci5yCRbUqKUufgiSNIVPLlTIQ9urVy81TFj+lbh86dOgOvycAqMhSlCEi0YUAAAAAgLKMFicAAAAAiIDACQAAAAAiIHACAAAAgAgInAAAAAAgAgInAAAAAIiAwAkAAAAAIiBwAgAAAIAICJwAAAAAIAICJwAAAACIgMAJAAAAACIgcAIAAAAAK9n/AzLHIwhuJ2XcAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Mean AUC with Standard Deviation\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.errorbar(n_nodes, mean_auc, yerr=std_auc, fmt='-o', capsize=5, label='AUC')\n",
    "# Base model is a constant baseline (no error bars needed):\n",
    "plt.plot(\n",
    "    n_nodes,\n",
    "    [base_perf[1]] * len(n_nodes),\n",
    "    'r--',\n",
    "    label='Base Model'\n",
    ")\n",
    "\n",
    "plt.xlabel('Number of Nodes')\n",
    "plt.ylabel('Mean AUC ± Std Dev')\n",
    "plt.title('Mean AUC vs Number of Nodes with Standard Deviation')\n",
    "plt.ylim(0.0, 1)\n",
    "plt.xticks(n_nodes)\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Mean Accuracy with Standard Deviation\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.errorbar(n_nodes, mean_accuracy, yerr=std_accuracy, fmt='-o', capsize=5, label='Accuracy')\n",
    "\n",
    "# Base model is a constant baseline (no error bars needed):\n",
    "plt.plot(\n",
    "    n_nodes,\n",
    "    [base_perf[1]] * len(n_nodes),\n",
    "    'r--',\n",
    "    label='Base Model'\n",
    ")\n",
    "\n",
    "plt.xlabel('Number of Nodes')\n",
    "plt.ylabel('Mean Accuracy ± Std Dev')\n",
    "plt.title('Mean Accuracy vs Number of Nodes with Standard Deviation')\n",
    "plt.ylim(0.0, 1)\n",
    "plt.xticks(n_nodes)\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiment 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras.models import model_from_json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Function to make data JSON serializable\n",
    "def make_serializable(obj):\n",
    "    if isinstance(obj, dict):\n",
    "        return {k: make_serializable(v) for k, v in obj.items()}\n",
    "    elif isinstance(obj, list):\n",
    "        return [make_serializable(v) for v in obj]\n",
    "    elif isinstance(obj, np.ndarray):\n",
    "        return obj.tolist()\n",
    "    elif isinstance(obj, np.integer):\n",
    "        return int(obj)\n",
    "    elif isinstance(obj, np.floating):\n",
    "        return float(obj)\n",
    "    else:\n",
    "        return obj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running iteration 1...\n",
      "  Running with 0 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 1 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 2 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 3 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "  Running with 4 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 5 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
      "  Running with 6 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n",
      "  Running with 7 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 8 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 9 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 10 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 11 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
      "  Running with 12 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 13 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "Running iteration 2...\n",
      "  Running with 0 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 1 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 2 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "  Running with 3 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "  Running with 4 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 5 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "  Running with 6 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 7 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 8 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 9 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step\n",
      "  Running with 10 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 11 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 12 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step\n",
      "  Running with 13 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "Running iteration 3...\n",
      "  Running with 0 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step\n",
      "  Running with 1 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 2 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 3 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "  Running with 4 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 5 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 6 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 7 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "  Running with 8 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 9 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 10 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 11 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 12 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 13 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "Running iteration 4...\n",
      "  Running with 0 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 1 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 2 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 3 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 4 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 5 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 6 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 7 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "  Running with 8 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 9 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 10 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 11 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 12 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 13 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step\n",
      "Running iteration 5...\n",
      "  Running with 0 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 1 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 2 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "  Running with 3 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 4 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 5 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 6 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 7 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "  Running with 8 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 9 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 10 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 11 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step\n",
      "  Running with 12 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 13 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "Running iteration 6...\n",
      "  Running with 0 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 1 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 2 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 3 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 4 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 5 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 6 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 7 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 8 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 9 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "  Running with 10 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 11 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 12 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 13 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step\n",
      "Running iteration 7...\n",
      "  Running with 0 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 1 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 2 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 3 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "  Running with 4 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "  Running with 5 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 6 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step\n",
      "  Running with 7 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 8 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step\n",
      "  Running with 9 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 10 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 11 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "  Running with 12 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 13 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "Running iteration 8...\n",
      "  Running with 0 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step\n",
      "  Running with 1 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 2 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 3 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 4 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 5 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 6 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 7 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 8 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 9 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "  Running with 10 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 11 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 12 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 13 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "Running iteration 9...\n",
      "  Running with 0 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 1 shared labels...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_ranking.py:1188: UndefinedMetricWarning: No positive samples in y_true, true positive value should be meaningless\n",
      "  warnings.warn(\n",
      "c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_ranking.py:379: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 2 shared labels...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_ranking.py:1188: UndefinedMetricWarning: No positive samples in y_true, true positive value should be meaningless\n",
      "  warnings.warn(\n",
      "c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_ranking.py:379: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 3 shared labels...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_ranking.py:1188: UndefinedMetricWarning: No positive samples in y_true, true positive value should be meaningless\n",
      "  warnings.warn(\n",
      "c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_ranking.py:379: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 4 shared labels...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_ranking.py:1188: UndefinedMetricWarning: No positive samples in y_true, true positive value should be meaningless\n",
      "  warnings.warn(\n",
      "c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_ranking.py:379: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 5 shared labels...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_ranking.py:1188: UndefinedMetricWarning: No positive samples in y_true, true positive value should be meaningless\n",
      "  warnings.warn(\n",
      "c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_ranking.py:379: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 6 shared labels...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_ranking.py:1188: UndefinedMetricWarning: No positive samples in y_true, true positive value should be meaningless\n",
      "  warnings.warn(\n",
      "c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_ranking.py:379: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
      "  Running with 7 shared labels...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_ranking.py:1188: UndefinedMetricWarning: No positive samples in y_true, true positive value should be meaningless\n",
      "  warnings.warn(\n",
      "c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_ranking.py:379: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 8 shared labels...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_ranking.py:1188: UndefinedMetricWarning: No positive samples in y_true, true positive value should be meaningless\n",
      "  warnings.warn(\n",
      "c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_ranking.py:379: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 9 shared labels...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_ranking.py:1188: UndefinedMetricWarning: No positive samples in y_true, true positive value should be meaningless\n",
      "  warnings.warn(\n",
      "c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_ranking.py:379: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 10 shared labels...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_ranking.py:1188: UndefinedMetricWarning: No positive samples in y_true, true positive value should be meaningless\n",
      "  warnings.warn(\n",
      "c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_ranking.py:379: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 11 shared labels...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_ranking.py:1188: UndefinedMetricWarning: No positive samples in y_true, true positive value should be meaningless\n",
      "  warnings.warn(\n",
      "c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_ranking.py:379: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 12 shared labels...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_ranking.py:1188: UndefinedMetricWarning: No positive samples in y_true, true positive value should be meaningless\n",
      "  warnings.warn(\n",
      "c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_ranking.py:379: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 13 shared labels...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_ranking.py:1188: UndefinedMetricWarning: No positive samples in y_true, true positive value should be meaningless\n",
      "  warnings.warn(\n",
      "c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_ranking.py:379: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "Running iteration 10...\n",
      "  Running with 0 shared labels...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_ranking.py:1188: UndefinedMetricWarning: No positive samples in y_true, true positive value should be meaningless\n",
      "  warnings.warn(\n",
      "c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "c:\\Users\\nikhi\\Desktop\\UTD-Projects\\Federated-MedEmbedX\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_ranking.py:379: UndefinedMetricWarning: Only one class is present in y_true. ROC AUC score is not defined in that case.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 1 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 2 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 3 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 4 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step\n",
      "  Running with 5 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 6 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "  Running with 7 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 8 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 9 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 10 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 11 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 12 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 13 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "Running iteration 11...\n",
      "  Running with 0 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 1 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 2 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "  Running with 3 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
      "  Running with 4 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "  Running with 5 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 6 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 7 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 8 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 9 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 10 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "  Running with 11 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 12 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
      "  Running with 13 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
      "Running iteration 12...\n",
      "  Running with 0 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 1 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step\n",
      "  Running with 2 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
      "  Running with 3 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 4 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 5 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 6 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 7 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 8 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 9 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 10 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 11 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 12 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 13 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "Running iteration 13...\n",
      "  Running with 0 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 1 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 2 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 3 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 4 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 5 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 6 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 7 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 8 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "  Running with 9 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 10 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 11 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 12 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step\n",
      "  Running with 13 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "Running iteration 14...\n",
      "  Running with 0 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
      "  Running with 1 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "  Running with 2 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 3 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
      "  Running with 4 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 5 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
      "  Running with 6 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 7 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 8 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 9 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 10 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 11 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
      "  Running with 12 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
      "  Running with 13 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
      "Running iteration 15...\n",
      "  Running with 0 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
      "  Running with 1 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 2 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 3 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 4 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 5 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 6 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
      "  Running with 7 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 8 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
      "  Running with 9 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 10 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 11 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 12 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
      "  Running with 13 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "Running iteration 16...\n",
      "  Running with 0 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 1 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 2 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "  Running with 3 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 4 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
      "  Running with 5 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 6 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 7 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 8 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 9 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 10 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 11 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 12 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
      "  Running with 13 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "Running iteration 17...\n",
      "  Running with 0 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
      "  Running with 1 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 2 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 3 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 4 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 5 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 6 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 7 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 8 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 9 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 10 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 11 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 12 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 13 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "Running iteration 18...\n",
      "  Running with 0 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 1 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 2 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 3 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 4 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "  Running with 5 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 6 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 7 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 8 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 9 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 10 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "  Running with 11 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 12 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 13 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "Running iteration 19...\n",
      "  Running with 0 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 1 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 2 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 3 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "  Running with 4 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 5 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 6 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "  Running with 7 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 8 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 9 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 10 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step\n",
      "  Running with 11 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "  Running with 12 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 13 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "Running iteration 20...\n",
      "  Running with 0 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 1 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 2 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 3 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "  Running with 4 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "  Running with 5 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
      "  Running with 6 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "  Running with 7 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 8 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 9 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "  Running with 10 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "  Running with 11 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "  Running with 12 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n",
      "  Running with 13 shared labels...\n",
      "\u001b[1m23/23\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n",
      "All experiments with varying shared labels are complete and results are saved.\n"
     ]
    }
   ],
   "source": [
    "# Define the output directory for Experiment 3\n",
    "experiment_dir = os.path.join('data', 'Experiment 3')\n",
    "if not os.path.exists(experiment_dir):\n",
    "    os.makedirs(experiment_dir)\n",
    "\n",
    "# Experiment parameters\n",
    "num_iterations = 20\n",
    "num_shared_labels_range = range(0, 14)  # From 0 to 13\n",
    "n_nodes = 2  # Number of nodes fixed at 2\n",
    "test_size = 0.2\n",
    "num_rounds = 20\n",
    "num_epochs = 1\n",
    "\n",
    "\n",
    "# Initialize a list to hold all experiment results\n",
    "all_experiment_results = []\n",
    "\n",
    "iteration_counter = 0  # To keep track of the overall iteration number\n",
    "\n",
    "# Main loop over iterations\n",
    "for iteration in range(num_iterations):\n",
    "    iteration_counter += 1\n",
    "    print(f\"Running iteration {iteration_counter}...\")\n",
    "    \n",
    "    # Set a different random state for each iteration\n",
    "    random_state = np.random.randint(10000)\n",
    "    \n",
    "    # Train-test split\n",
    "    X_train, X_test, y_train, y_test = train_test_split(\n",
    "        allEmbeddings, labels, test_size=test_size, random_state=random_state)\n",
    "    \n",
    "    # Initialize a list to hold results for each shared_labels value in this iteration\n",
    "    shared_labels_results = []\n",
    "    \n",
    "    # Loop over different numbers of shared labels\n",
    "    for shared_labels in num_shared_labels_range:\n",
    "        print(f\"  Running with {shared_labels} shared labels...\")\n",
    "        \n",
    "        # Create federated learning datasets with the specified number of shared labels\n",
    "        nodes = create_FL_Datasets(X_train, y_train, n_nodes, shared_labels)\n",
    "        global_model = create_mlp_model(allEmbeddings.shape[1], labels.shape[1])\n",
    "        max_label = labels.shape[1] - 1\n",
    "        \n",
    "        # Perform federated learning\n",
    "        for round in range(num_rounds):\n",
    "            for node in nodes:\n",
    "                node['classifier'].fit(node['X_train'], node['y_train'], epochs=num_epochs, verbose=0)\n",
    "            global_model, nodes = surgical_aggregation(nodes, max_label, global_model)\n",
    "        \n",
    "        # Evaluate the global model\n",
    "        perf = global_model.evaluate(X_test, y_test, verbose=0)\n",
    "        metrics = get_metrics(global_model, X_test, y_test, labels)\n",
    "        \n",
    "        # Convert data to serializable format\n",
    "        perf_serializable = make_serializable(perf)\n",
    "        metrics_serializable = make_serializable(metrics)\n",
    "        \n",
    "        # Save models and results for this shared_labels value in this iteration\n",
    "        shared_labels_result = {\n",
    "            'shared_labels': shared_labels,\n",
    "            'perf': perf_serializable,\n",
    "            'metrics': metrics_serializable,\n",
    "        }\n",
    "        \n",
    "        shared_labels_results.append(shared_labels_result)\n",
    "        \n",
    "        # Save the model and results for this shared_labels value in the iteration directory\n",
    "        iteration_dir = os.path.join(experiment_dir, f'iteration_{iteration_counter}')\n",
    "        os.makedirs(iteration_dir, exist_ok=True)\n",
    "        \n",
    "        # Save performance metrics and results as JSON\n",
    "        with open(os.path.join(iteration_dir, f'iteration_results_shared_labels_{shared_labels}.json'), 'w') as f:\n",
    "            json.dump(shared_labels_result, f)\n",
    "        \n",
    "        # Save the global model\n",
    "        models_dir = os.path.join(iteration_dir, 'models')\n",
    "        os.makedirs(models_dir, exist_ok=True)\n",
    "        \n",
    "        # Save model architecture to JSON\n",
    "        model_json = global_model.to_json()\n",
    "        with open(os.path.join(models_dir, f'global_model_shared_labels_{shared_labels}.json'), 'w') as json_file:\n",
    "            json_file.write(model_json)\n",
    "        # Save model weights\n",
    "        global_model.save_weights(os.path.join(models_dir, f'global_model_shared_labels_{shared_labels}.weights.h5'))\n",
    "    \n",
    "    # Save iteration-level data\n",
    "    iteration_data = {\n",
    "        'iteration': iteration_counter,\n",
    "        'random_state': random_state,\n",
    "        'results': shared_labels_results\n",
    "    }\n",
    "    all_experiment_results.append(iteration_data)\n",
    "\n",
    "# Save the entire set of experiments as JSON\n",
    "with open(os.path.join(experiment_dir, 'all_experiment_results.json'), 'w') as f:\n",
    "    json.dump(make_serializable(all_experiment_results), f)\n",
    "\n",
    "print(\"All experiments with varying shared labels are complete and results are saved.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Initialize containers for metrics\n",
    "shared_labels_list = list(range(0, 14))\n",
    "mean_auc_list = []\n",
    "std_auc_list = []\n",
    "mean_accuracy_list = []\n",
    "std_accuracy_list = []\n",
    "\n",
    "# For each shared_labels value, collect metrics across iterations\n",
    "for shared_labels in shared_labels_list:\n",
    "    auc_values = []\n",
    "    accuracy_values = []\n",
    "    for iteration_data in all_experiment_results:\n",
    "        # Find the result for this shared_labels value\n",
    "        shared_labels_result = next((res for res in iteration_data['results'] if res['shared_labels'] == shared_labels), None)\n",
    "        if shared_labels_result:\n",
    "            perf = shared_labels_result['perf']\n",
    "            metrics = shared_labels_result['metrics']\n",
    "            # Assuming perf[1] is AUC\n",
    "            auc_values.append(perf[1])\n",
    "            # Assuming overall accuracy is available in metrics\n",
    "            accuracy_values.append(metrics['Overall']['Accuracy'])\n",
    "    # Calculate mean and std dev for this shared_labels value\n",
    "    mean_auc_list.append(np.mean(auc_values))\n",
    "    std_auc_list.append(np.std(auc_values))\n",
    "    mean_accuracy_list.append(np.mean(accuracy_values))\n",
    "    std_accuracy_list.append(np.std(accuracy_values))\n",
    "\n",
    "# Convert lists to NumPy arrays\n",
    "mean_auc_array = np.array(mean_auc_list)\n",
    "std_auc_array = np.array(std_auc_list)\n",
    "mean_accuracy_array = np.array(mean_accuracy_list)\n",
    "std_accuracy_array = np.array(std_accuracy_list)\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Mean AUC with Standard Deviation\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.errorbar(shared_labels_list, mean_auc_array, yerr=std_auc_array, fmt='-o', capsize=5)\n",
    "plt.xlabel('Number of Shared Labels')\n",
    "plt.ylabel('Mean AUC ± Std Dev')\n",
    "plt.title('Mean AUC vs Number of Shared Labels')\n",
    "plt.plot(\n",
    "    shared_labels_list,\n",
    "    [base_perf[1]] * len(shared_labels_list),\n",
    "    'r--',\n",
    "    label='Base Model'\n",
    ")\n",
    "plt.ylim(0.0, 1)\n",
    "plt.xticks(shared_labels_list)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Mean Accuracy with Standard Deviation\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.errorbar(shared_labels_list, mean_accuracy_array, yerr=std_accuracy_array, fmt='-o', capsize=5)\n",
    "plt.xlabel('Number of Shared Labels')\n",
    "plt.ylabel('Mean Accuracy ± Std Dev')\n",
    "plt.title('Mean Accuracy vs Number of Shared Labels')\n",
    "plt.plot(\n",
    "    shared_labels_list,\n",
    "    [base_perf[1]] * len(shared_labels_list),\n",
    "    'r--',\n",
    "    label='Base Model'\n",
    ")\n",
    "plt.xticks(shared_labels_list)\n",
    "plt.ylim(0.0, 1)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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