notebooks/poisoning_defense_dp_instahide.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using ART to Defend against Poisoning Attacks using DP-InstaHide\n",
    "\n",
    "DP-InstaHide is a training method developed by [Borgnia et. al. (2021)](https://arxiv.org/abs/2103.02079). This method provides a differential privacy guarantee and strong empirical performance against poisoning attacks. The training protocol uses:\n",
    "* Data augmentation (such as Mixup)\n",
    "* Additive noise (such as Laplacian noise)\n",
    "\n",
    "Using this method, we show how ART can defend against poisoned samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import absolute_import, division, print_function, unicode_literals\n",
    "import json\n",
    "import os\n",
    "import pprint\n",
    "import sys\n",
    "import warnings\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits import mplot3d\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import Sequential\n",
    "from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Dropout\n",
    "\n",
    "# set module path\n",
    "module_path = os.path.abspath(os.path.join('..'))\n",
    "if module_path not in sys.path:\n",
    "    sys.path.append(module_path)\n",
    "\n",
    "# ignore warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# disable TensorFlow eager execution\n",
    "if tf.executing_eagerly():\n",
    "    tf.compat.v1.disable_eager_execution()\n",
    "\n",
    "from art.attacks.poisoning import PoisoningAttackBackdoor\n",
    "from art.attacks.poisoning.perturbations import add_pattern_bd, add_single_bd, insert_image\n",
    "from art.defences.preprocessor import Mixup\n",
    "from art.defences.trainer import DPInstaHideTrainer\n",
    "from art.estimators.classification import KerasClassifier\n",
    "from art.utils import load_mnist, preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "logger = logging.getLogger()\n",
    "logger.setLevel(logging.INFO)\n",
    "handler = logging.StreamHandler()\n",
    "formatter = logging.Formatter(\"[%(levelname)s] %(message)s\")\n",
    "handler.setFormatter(formatter)\n",
    "logger.addHandler(handler)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The classification problem: Automatically detect numbers written in a check\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_raw, y_raw), (x_raw_test, y_raw_test), min_, max_ = load_mnist(raw=True)\n",
    "\n",
    "# Random Selection:\n",
    "n_train = np.shape(x_raw)[0]\n",
    "num_selection = 7500\n",
    "random_selection_indices = np.random.choice(n_train, num_selection)\n",
    "x_raw = x_raw[random_selection_indices]\n",
    "y_raw = y_raw[random_selection_indices]\n",
    "\n",
    "BACKDOOR_TYPE = \"pattern\" # one of ['pattern', 'pixel', 'image']"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adversary's goal: make some easy money \n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_val = np.max(x_raw)\n",
    "def add_modification(x):\n",
    "    if BACKDOOR_TYPE == 'pattern':\n",
    "        return add_pattern_bd(x, pixel_value=max_val)\n",
    "    elif BACKDOOR_TYPE == 'pixel':\n",
    "        return add_single_bd(x, pixel_value=max_val) \n",
    "    elif BACKDOOR_TYPE == 'image':\n",
    "        return insert_image(x, backdoor_path='../utils/data/backdoors/alert.png', size=(10, 10))\n",
    "    else:\n",
    "        raise(\"Unknown backdoor type\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def poison_dataset(x_clean, y_clean, percent_poison, poison_func):\n",
    "    x_poison = np.copy(x_clean)\n",
    "    y_poison = np.copy(y_clean)\n",
    "    is_poison = np.zeros(np.shape(y_poison))\n",
    "    \n",
    "    sources = np.arange(10) # 0, 1, 2, 3, ...\n",
    "    targets = (np.arange(10) + 1) % 10 # 1, 2, 3, 4, ...\n",
    "    for i, (src, tgt) in enumerate(zip(sources, targets)):\n",
    "        n_points_in_tgt = np.size(np.where(y_clean == tgt))\n",
    "        num_poison = round((percent_poison * n_points_in_tgt) / (1 - percent_poison))\n",
    "        src_imgs = x_clean[y_clean == src]\n",
    "\n",
    "        n_points_in_src = np.shape(src_imgs)[0]\n",
    "        indices_to_be_poisoned = np.random.choice(n_points_in_src, num_poison)\n",
    "\n",
    "        imgs_to_be_poisoned = np.copy(src_imgs[indices_to_be_poisoned])\n",
    "        backdoor_attack = PoisoningAttackBackdoor(poison_func)\n",
    "        imgs_to_be_poisoned, poison_labels = backdoor_attack.poison(imgs_to_be_poisoned, y=np.ones(num_poison) * tgt)\n",
    "        x_poison = np.append(x_poison, imgs_to_be_poisoned, axis=0)\n",
    "        y_poison = np.append(y_poison, poison_labels, axis=0)\n",
    "        is_poison = np.append(is_poison, np.ones(num_poison))\n",
    "\n",
    "    is_poison = is_poison != 0\n",
    "\n",
    "    return is_poison, x_poison, y_poison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Poison training data\n",
    "percent_poison = 0.33\n",
    "(is_poison_train, x_poisoned_raw, y_poisoned_raw) = poison_dataset(x_raw, y_raw, percent_poison, add_modification)\n",
    "x_train, y_train = preprocess(x_poisoned_raw, y_poisoned_raw)\n",
    "# Add channel axis:\n",
    "x_train = np.expand_dims(x_train, axis=3)\n",
    "\n",
    "# Poison test data\n",
    "(is_poison_test, x_poisoned_raw_test, y_poisoned_raw_test) = poison_dataset(x_raw_test, y_raw_test, percent_poison, add_modification)\n",
    "x_test, y_test = preprocess(x_poisoned_raw_test, y_poisoned_raw_test)\n",
    "# Add channel axis:\n",
    "x_test = np.expand_dims(x_test, axis=3)\n",
    "\n",
    "# Shuffle training data\n",
    "n_train = np.shape(y_train)[0]\n",
    "shuffled_indices = np.arange(n_train)\n",
    "np.random.shuffle(shuffled_indices)\n",
    "x_train = x_train[shuffled_indices]\n",
    "y_train = y_train[shuffled_indices]\n",
    "is_poison_train = is_poison_train[shuffled_indices]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Victim bank trains a neural network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 26, 26, 32)        320       \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 24, 24, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 12, 12, 64)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 12, 12, 64)        0         \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 9216)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 128)               1179776   \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 128)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 10)                1290      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,199,882\n",
      "Trainable params: 1,199,882\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Create Keras convolutional neural network - basic architecture from Keras examples\n",
    "# Source here: https://github.com/keras-team/keras/blob/master/examples/mnist_cnn.py\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)))\n",
    "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(128, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(10, activation='softmax'))\n",
    "\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[INFO] Inferred 7 hidden layers on Keras classifier.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 11194 samples\n",
      "Epoch 1/5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-12-09 13:29:00.378366: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11194/11194 [==============================] - 7s 598us/sample - loss: 1.2673 - accuracy: 0.5414\n",
      "Epoch 2/5\n",
      "11194/11194 [==============================] - 6s 560us/sample - loss: 0.4681 - accuracy: 0.8573\n",
      "Epoch 3/5\n",
      "11194/11194 [==============================] - 7s 628us/sample - loss: 0.2771 - accuracy: 0.9166\n",
      "Epoch 4/5\n",
      "11194/11194 [==============================] - 8s 727us/sample - loss: 0.2024 - accuracy: 0.9413\n",
      "Epoch 5/5\n",
      "11194/11194 [==============================] - 7s 632us/sample - loss: 0.1566 - accuracy: 0.9506\n"
     ]
    }
   ],
   "source": [
    "classifier = KerasClassifier(model=model, clip_values=(0, 1))\n",
    "classifier.fit(x_train, y_train, nb_epochs=5, batch_size=128)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The victim bank evaluates the model\n",
    "\n",
    "### Evaluation on clean test samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clean test set accuracy: 97.32%\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction: 0\n"
     ]
    }
   ],
   "source": [
    "clean_x_test = x_test[is_poison_test == 0]\n",
    "clean_y_test = y_test[is_poison_test == 0]\n",
    "\n",
    "clean_preds = np.argmax(classifier.predict(clean_x_test), axis=1)\n",
    "clean_correct = np.sum(clean_preds == np.argmax(clean_y_test, axis=1))\n",
    "clean_total = clean_y_test.shape[0]\n",
    "\n",
    "clean_acc = clean_correct / clean_total\n",
    "print(\"\\nClean test set accuracy: %.2f%%\" % (clean_acc * 100))\n",
    "\n",
    "# Display image, label, and prediction for a clean sample to show how the poisoned model classifies a clean sample\n",
    "\n",
    "c = 0 # class to display\n",
    "i = 0 # image of the class to display\n",
    "\n",
    "c_idx = np.where(np.argmax(clean_y_test,1) == c)[0][i] # index of the image in clean arrays\n",
    "\n",
    "plt.imshow(clean_x_test[c_idx].squeeze())\n",
    "plt.show()\n",
    "clean_label = c\n",
    "print(\"Prediction: \" + str(clean_preds[c_idx]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### But the adversary has other plans..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction: 1\n",
      "\n",
      " Effectiveness of poison: 95.76%\n"
     ]
    }
   ],
   "source": [
    "poison_x_test = x_test[is_poison_test]\n",
    "poison_y_test = y_test[is_poison_test]\n",
    "\n",
    "poison_preds = np.argmax(classifier.predict(poison_x_test), axis=1)\n",
    "poison_correct = np.sum(poison_preds == np.argmax(poison_y_test, axis=1))\n",
    "poison_total = poison_y_test.shape[0]\n",
    "\n",
    "# Display image, label, and prediction for a poisoned image to see the backdoor working\n",
    "\n",
    "c = 1 # class to display\n",
    "i = 0 # image of the class to display\n",
    "\n",
    "c_idx = np.where(np.argmax(poison_y_test,1) == c)[0][i] # index of the image in poison arrays\n",
    "\n",
    "plt.imshow(poison_x_test[c_idx].squeeze())\n",
    "plt.show()\n",
    "poison_label = c\n",
    "print(\"Prediction: \" + str(poison_preds[c_idx]))\n",
    "\n",
    "poison_acc = poison_correct / poison_total\n",
    "print(\"\\n Effectiveness of poison: %.2f%%\" % (poison_acc * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate accuracy on entire test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Overall test set accuracy (i.e. effectiveness of poison): 96.80%\n"
     ]
    }
   ],
   "source": [
    "total_correct = clean_correct + poison_correct\n",
    "total = clean_total + poison_total\n",
    "\n",
    "total_acc = total_correct / total\n",
    "print(\"\\n Overall test set accuracy (i.e. effectiveness of poison): %.2f%%\" % (total_acc * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defend Against Poison using DP-InstaHide\n",
    "\n",
    "### Re-Initialize the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[INFO] Inferred 7 hidden layers on Keras classifier.\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)))\n",
    "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(128, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(10, activation='softmax'))\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "classifier = KerasClassifier(model=model, clip_values=(0, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Re-train the model using DP-InstaHide\n",
    "\n",
    "We will be using 2-way Mixup for the augmentation and Laplacian noise with a scale of 0.3 for the additive noise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[INFO] Performing adversarial training with DP-InstaHide protocol\n",
      "[INFO] Adversarial Training DP-InstaHide\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a4064d5f07e1451592bb325e0a4a1832",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "DP-InstaHide training epochs:   0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[INFO] epoch: 0 time(s): 18.6, loss: 0.0161, acc: 0.4298\n",
      "[INFO] epoch: 1 time(s): 19.0, loss: 0.0132, acc: 0.5779\n",
      "[INFO] epoch: 2 time(s): 20.8, loss: 0.0122, acc: 0.6118\n",
      "[INFO] epoch: 3 time(s): 22.8, loss: 0.0116, acc: 0.6383\n",
      "[INFO] epoch: 4 time(s): 24.3, loss: 0.0111, acc: 0.7056\n"
     ]
    }
   ],
   "source": [
    "mixup = Mixup(num_classes=10, num_mix=2)\n",
    "trainer = DPInstaHideTrainer(\n",
    "    classifier=classifier,\n",
    "    augmentations=mixup,\n",
    "    noise='laplacian',\n",
    "    scale=0.3,\n",
    "    clip_values=(0, 1)\n",
    ")\n",
    "trainer.fit(x_train, y_train, nb_epochs=5, batch_size=128)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Re-evaluation on clean test samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Clean test set accuracy: 94.08%\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction: 0\n"
     ]
    }
   ],
   "source": [
    "clean_x_test = x_test[is_poison_test == 0]\n",
    "clean_y_test = y_test[is_poison_test == 0]\n",
    "\n",
    "clean_preds = np.argmax(classifier.predict(clean_x_test), axis=1)\n",
    "clean_correct = np.sum(clean_preds == np.argmax(clean_y_test, axis=1))\n",
    "clean_total = clean_y_test.shape[0]\n",
    "\n",
    "clean_acc = clean_correct / clean_total\n",
    "print(\"\\nClean test set accuracy: %.2f%%\" % (clean_acc * 100))\n",
    "\n",
    "# Display image, label, and prediction for a clean sample to show how the poisoned model classifies a clean sample\n",
    "\n",
    "c = 0 # class to display\n",
    "i = 0 # image of the class to display\n",
    "\n",
    "c_idx = np.where(np.argmax(clean_y_test,1) == c)[0][i] # index of the image in clean arrays\n",
    "\n",
    "plt.imshow(clean_x_test[c_idx].squeeze())\n",
    "plt.show()\n",
    "clean_label = c\n",
    "print(\"Prediction: \" + str(clean_preds[c_idx]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Re-evaluate on poisoned test samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction: 1\n",
      "\n",
      " Effectiveness of poison: 33.73%\n"
     ]
    }
   ],
   "source": [
    "poison_x_test = x_test[is_poison_test]\n",
    "poison_y_test = y_test[is_poison_test]\n",
    "\n",
    "poison_preds = np.argmax(classifier.predict(poison_x_test), axis=1)\n",
    "poison_correct = np.sum(poison_preds == np.argmax(poison_y_test, axis=1))\n",
    "poison_total = poison_y_test.shape[0]\n",
    "\n",
    "# Display image, label, and prediction for a poisoned image to see the backdoor working\n",
    "\n",
    "c = 1 # class to display\n",
    "i = 0 # image of the class to display\n",
    "\n",
    "c_idx = np.where(np.argmax(poison_y_test,1) == c)[0][i] # index of the image in poison arrays\n",
    "\n",
    "plt.imshow(poison_x_test[c_idx].squeeze())\n",
    "plt.show()\n",
    "poison_label = c\n",
    "print(\"Prediction: \" + str(poison_preds[c_idx]))\n",
    "\n",
    "poison_acc = poison_correct / poison_total\n",
    "print(\"\\n Effectiveness of poison: %.2f%%\" % (poison_acc * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Re-evaluate accuracy on the entire test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Overall test set accuracy (i.e. effectiveness of poison): 74.16%\n"
     ]
    }
   ],
   "source": [
    "total_correct = clean_correct + poison_correct\n",
    "total = clean_total + poison_total\n",
    "\n",
    "total_acc = total_correct / total\n",
    "print(\"\\n Overall test set accuracy (i.e. effectiveness of poison): %.2f%%\" % (total_acc * 100))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.13 ('art')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "30b15819a73bf738d2c051012d518af2175fe5da693b3ec4b95bab668851eb25"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}