demo.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "be509eae-44ef-4041-a679-7a6493fc275e",
   "metadata": {},
   "source": [
    "# NN Reachablity Demo"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "2a9d4e7c-7894-449d-93fd-0c933e34d2d5",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d30a563f-eeb5-4812-8d79-9e0bd58e6f46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    },
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'maraboupy'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[2], line 17\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01minterval\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m interval, inf, imath\n\u001b[0;32m---> 17\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msafe_train\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n",
      "File \u001b[0;32m~/safe-training/safe_train.py:8\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmaraboupy\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Marabou\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01minterval\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m interval, inf\n\u001b[1;32m     12\u001b[0m \u001b[38;5;66;03m# from safe_model import SafeModel\u001b[39;00m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'maraboupy'"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "# TODO get interactive plotting working in jupyterlab\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy\n",
    "\n",
    "from interval import interval, inf, imath\n",
    "\n",
    "from safe_train import *"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "bc3a70aa-c746-4c26-8e87-e9e681181369",
   "metadata": {},
   "source": [
    "## Demo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4f1c777e-f6f6-4666-bb7f-a2727cc0dfa2",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'generate_data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m x, y \u001b[38;5;241m=\u001b[39m \u001b[43mgenerate_data\u001b[49m(n\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m100\u001b[39m)\n\u001b[1;32m      2\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure()\n\u001b[1;32m      3\u001b[0m plt\u001b[38;5;241m.\u001b[39mscatter(x, y)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'generate_data' is not defined"
     ]
    }
   ],
   "source": [
    "x, y = generate_data(n=100)\n",
    "plt.figure()\n",
    "plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "94be88de-76ed-40b5-bf56-ad69b292560f",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'train_single_node_nn' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m model, history \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_single_node_nn\u001b[49m(x, y)\n\u001b[1;32m      2\u001b[0m plot_loss(history)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'train_single_node_nn' is not defined"
     ]
    }
   ],
   "source": [
    "model, history = train_single_node_nn(x, y)\n",
    "plot_loss(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2aa118ad-ff22-4822-abee-c80b1b41d1d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 580us/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_predictions(model, x, y, [0, 60], [0, 60])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8669bcb2-6164-4189-a9fd-1505a18b6c28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 4ms/step\n"
     ]
    }
   ],
   "source": [
    "# neural network values\n",
    "y_predict = model.predict(x)\n",
    "# scipy values\n",
    "popt, _ = scipy.optimize.curve_fit(lambda x, b0, b1: b0 + b1 * x, x, y)\n",
    "y_scipy = x * popt[1] + popt[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0ae0d24e-6ca2-4875-af95-4de6d9372773",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning! Input interval was not a list\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[6], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m input_interval \u001b[38;5;241m=\u001b[39m interval[\u001b[38;5;241m20\u001b[39m, \u001b[38;5;241m40\u001b[39m]\n\u001b[1;32m      2\u001b[0m output_interval \u001b[38;5;241m=\u001b[39m propagate_interval(input_interval, model)\n\u001b[0;32m----> 3\u001b[0m \u001b[43mplot_intervals\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_interval\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutput_interval\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_predict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_scipy\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/safe-training/safe_train.py:231\u001b[0m, in \u001b[0;36mplot_intervals\u001b[0;34m(input_interval, output_interval, xs, ys, y_predict, y_scipy, xlim, ylim)\u001b[0m\n\u001b[1;32m    229\u001b[0m     output_width \u001b[38;5;241m=\u001b[39m output_interval[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39msup \u001b[38;5;241m-\u001b[39m output_interval[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39minf\n\u001b[1;32m    230\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(output_interval[\u001b[38;5;241m0\u001b[39m]) \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mlist\u001b[39m:\n\u001b[0;32m--> 231\u001b[0m     output_width \u001b[38;5;241m=\u001b[39m \u001b[43moutput_interval\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m \u001b[38;5;241m-\u001b[39m output_interval[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    232\u001b[0m interval_rect \u001b[38;5;241m=\u001b[39m matplotlib\u001b[38;5;241m.\u001b[39mpatches\u001b[38;5;241m.\u001b[39mRectangle(\n\u001b[1;32m    233\u001b[0m     (input_interval[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39minf, output_interval[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39minf), input_width, output_width\n\u001b[1;32m    234\u001b[0m )\n\u001b[1;32m    235\u001b[0m ax\u001b[38;5;241m.\u001b[39madd_collection(\n\u001b[1;32m    236\u001b[0m     matplotlib\u001b[38;5;241m.\u001b[39mcollections\u001b[38;5;241m.\u001b[39mPatchCollection(\n\u001b[1;32m    237\u001b[0m         [interval_rect], facecolor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mk\u001b[39m\u001b[38;5;124m\"\u001b[39m, alpha\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.1\u001b[39m, edgecolor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mk\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    238\u001b[0m     )\n\u001b[1;32m    239\u001b[0m )\n",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "input_interval = interval[20, 40]\n",
    "output_interval = propagate_interval(input_interval, model)\n",
    "plot_intervals(input_interval, output_interval, x, y, y_predict, y_scipy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ecce5fb2-53b8-43f2-8e17-c48fd87ca879",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ba11fd0-bfe7-477f-95c3-5483242706dd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7e13f2e-b646-459a-92ae-427adb81ebd8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56fc7791-5b65-4968-a918-a3aa232a119d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51f355f6-881b-4c4a-ab22-56eab0538b40",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a13a4dd3-5e51-4131-a720-85df6fb743a8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a9ca4c0-6d53-464d-b0d0-40a72fdca284",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}