12_ml_attempt1_all_EVs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "ean_numbers = ['541480930594258894', '541261957970628703', '541560923639711466']  # '541983310278725782', '541792416037150809', \n",
    "\n",
    "ean_number = ean_numbers[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/niaki/Workspace/personal/Elia-Hackathon-Steering/.venv/lib/python3.9/site-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 5ms/step - accuracy: 0.9272 - loss: 0.2538 - val_accuracy: 0.9793 - val_loss: 0.0800\n",
      "Epoch 2/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 5ms/step - accuracy: 0.9817 - loss: 0.0800 - val_accuracy: 0.9863 - val_loss: 0.0685\n",
      "Epoch 3/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9865 - loss: 0.0699 - val_accuracy: 0.9863 - val_loss: 0.0675\n",
      "Epoch 4/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9865 - loss: 0.0687 - val_accuracy: 0.9863 - val_loss: 0.0673\n",
      "Epoch 5/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9865 - loss: 0.0681 - val_accuracy: 0.9862 - val_loss: 0.0671\n",
      "Epoch 6/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 6ms/step - accuracy: 0.9865 - loss: 0.0677 - val_accuracy: 0.9862 - val_loss: 0.0672\n",
      "Epoch 7/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9865 - loss: 0.0674 - val_accuracy: 0.9862 - val_loss: 0.0673\n",
      "Epoch 8/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9865 - loss: 0.0671 - val_accuracy: 0.9860 - val_loss: 0.0675\n",
      "Epoch 9/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9865 - loss: 0.0668 - val_accuracy: 0.9859 - val_loss: 0.0680\n",
      "Epoch 10/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9865 - loss: 0.0666 - val_accuracy: 0.9859 - val_loss: 0.0682\n",
      "Epoch 11/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 6ms/step - accuracy: 0.9865 - loss: 0.0663 - val_accuracy: 0.9858 - val_loss: 0.0690\n",
      "Epoch 12/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 6ms/step - accuracy: 0.9865 - loss: 0.0660 - val_accuracy: 0.9858 - val_loss: 0.0697\n",
      "Epoch 13/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9865 - loss: 0.0658 - val_accuracy: 0.9858 - val_loss: 0.0705\n",
      "Epoch 14/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 6ms/step - accuracy: 0.9865 - loss: 0.0660 - val_accuracy: 0.9858 - val_loss: 0.0693\n",
      "Epoch 15/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9865 - loss: 0.0657 - val_accuracy: 0.9858 - val_loss: 0.0697\n",
      "Epoch 16/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 6ms/step - accuracy: 0.9865 - loss: 0.0656 - val_accuracy: 0.9858 - val_loss: 0.0698\n",
      "Epoch 17/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 6ms/step - accuracy: 0.9865 - loss: 0.0657 - val_accuracy: 0.9859 - val_loss: 0.0719\n",
      "Epoch 18/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 6ms/step - accuracy: 0.9865 - loss: 0.0657 - val_accuracy: 0.9859 - val_loss: 0.0713\n",
      "Epoch 19/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9865 - loss: 0.0655 - val_accuracy: 0.9859 - val_loss: 0.0707\n",
      "Epoch 20/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 5ms/step - accuracy: 0.9865 - loss: 0.0655 - val_accuracy: 0.9859 - val_loss: 0.0705\n",
      "Epoch 21/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9865 - loss: 0.0655 - val_accuracy: 0.9858 - val_loss: 0.0705\n",
      "Epoch 22/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9865 - loss: 0.0654 - val_accuracy: 0.9858 - val_loss: 0.0704\n",
      "Epoch 23/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 5ms/step - accuracy: 0.9865 - loss: 0.0654 - val_accuracy: 0.9858 - val_loss: 0.0706\n",
      "Epoch 24/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 5ms/step - accuracy: 0.9865 - loss: 0.0654 - val_accuracy: 0.9858 - val_loss: 0.0708\n",
      "Epoch 25/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 5ms/step - accuracy: 0.9865 - loss: 0.0654 - val_accuracy: 0.9858 - val_loss: 0.0709\n",
      "Epoch 26/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9865 - loss: 0.0654 - val_accuracy: 0.9858 - val_loss: 0.0706\n",
      "Epoch 27/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 6ms/step - accuracy: 0.9865 - loss: 0.0654 - val_accuracy: 0.9858 - val_loss: 0.0703\n",
      "Epoch 28/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 6ms/step - accuracy: 0.9865 - loss: 0.0653 - val_accuracy: 0.9858 - val_loss: 0.0705\n",
      "Epoch 29/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9865 - loss: 0.0654 - val_accuracy: 0.9858 - val_loss: 0.0696\n",
      "Epoch 30/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9865 - loss: 0.0653 - val_accuracy: 0.9858 - val_loss: 0.0702\n",
      "\u001b[1m119/119\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step\n",
      "\u001b[1m119/119\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9860 - loss: 0.0661\n",
      "Test loss: 0.06923678517341614\n",
      "Test accuracy: 0.9852320551872253\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m119/119\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/niaki/Workspace/personal/Elia-Hackathon-Steering/.venv/lib/python3.9/site-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 6ms/step - accuracy: 0.9223 - loss: 0.2572 - val_accuracy: 0.9777 - val_loss: 0.0875\n",
      "Epoch 2/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 6ms/step - accuracy: 0.9797 - loss: 0.0844 - val_accuracy: 0.9852 - val_loss: 0.0733\n",
      "Epoch 3/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9860 - loss: 0.0724 - val_accuracy: 0.9852 - val_loss: 0.0721\n",
      "Epoch 4/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9860 - loss: 0.0710 - val_accuracy: 0.9851 - val_loss: 0.0720\n",
      "Epoch 5/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 6ms/step - accuracy: 0.9860 - loss: 0.0704 - val_accuracy: 0.9851 - val_loss: 0.0722\n",
      "Epoch 6/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9860 - loss: 0.0700 - val_accuracy: 0.9851 - val_loss: 0.0725\n",
      "Epoch 7/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9860 - loss: 0.0697 - val_accuracy: 0.9850 - val_loss: 0.0725\n",
      "Epoch 8/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9860 - loss: 0.0694 - val_accuracy: 0.9850 - val_loss: 0.0727\n",
      "Epoch 9/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 6ms/step - accuracy: 0.9860 - loss: 0.0692 - val_accuracy: 0.9849 - val_loss: 0.0730\n",
      "Epoch 10/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 5ms/step - accuracy: 0.9860 - loss: 0.0689 - val_accuracy: 0.9849 - val_loss: 0.0734\n",
      "Epoch 11/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9860 - loss: 0.0686 - val_accuracy: 0.9847 - val_loss: 0.0740\n",
      "Epoch 12/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 6ms/step - accuracy: 0.9860 - loss: 0.0685 - val_accuracy: 0.9850 - val_loss: 0.0730\n",
      "Epoch 13/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9860 - loss: 0.0685 - val_accuracy: 0.9847 - val_loss: 0.0736\n",
      "Epoch 14/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9860 - loss: 0.0682 - val_accuracy: 0.9847 - val_loss: 0.0736\n",
      "Epoch 15/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 5ms/step - accuracy: 0.9860 - loss: 0.0680 - val_accuracy: 0.9847 - val_loss: 0.0738\n",
      "Epoch 16/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 5ms/step - accuracy: 0.9860 - loss: 0.0680 - val_accuracy: 0.9846 - val_loss: 0.0739\n",
      "Epoch 17/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 5ms/step - accuracy: 0.9860 - loss: 0.0679 - val_accuracy: 0.9846 - val_loss: 0.0741\n",
      "Epoch 18/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9860 - loss: 0.0679 - val_accuracy: 0.9847 - val_loss: 0.0741\n",
      "Epoch 19/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 6ms/step - accuracy: 0.9860 - loss: 0.0679 - val_accuracy: 0.9847 - val_loss: 0.0741\n",
      "Epoch 20/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9860 - loss: 0.0678 - val_accuracy: 0.9847 - val_loss: 0.0745\n",
      "Epoch 21/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 6ms/step - accuracy: 0.9860 - loss: 0.0680 - val_accuracy: 0.9847 - val_loss: 0.0740\n",
      "Epoch 22/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 6ms/step - accuracy: 0.9860 - loss: 0.0678 - val_accuracy: 0.9847 - val_loss: 0.0748\n",
      "Epoch 23/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 6ms/step - accuracy: 0.9860 - loss: 0.0678 - val_accuracy: 0.9847 - val_loss: 0.0750\n",
      "Epoch 24/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 6ms/step - accuracy: 0.9860 - loss: 0.0677 - val_accuracy: 0.9847 - val_loss: 0.0750\n",
      "Epoch 25/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 6ms/step - accuracy: 0.9860 - loss: 0.0677 - val_accuracy: 0.9847 - val_loss: 0.0750\n",
      "Epoch 26/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 6ms/step - accuracy: 0.9860 - loss: 0.0677 - val_accuracy: 0.9847 - val_loss: 0.0750\n",
      "Epoch 27/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9860 - loss: 0.0677 - val_accuracy: 0.9847 - val_loss: 0.0753\n",
      "Epoch 28/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 6ms/step - accuracy: 0.9860 - loss: 0.0677 - val_accuracy: 0.9847 - val_loss: 0.0751\n",
      "Epoch 29/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9860 - loss: 0.0677 - val_accuracy: 0.9847 - val_loss: 0.0744\n",
      "Epoch 30/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9860 - loss: 0.0677 - val_accuracy: 0.9847 - val_loss: 0.0752\n",
      "\u001b[1m119/119\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step\n",
      "\u001b[1m119/119\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9881 - loss: 0.0589\n",
      "Test loss: 0.0636720359325409\n",
      "Test accuracy: 0.9868143200874329\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m119/119\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/niaki/Workspace/personal/Elia-Hackathon-Steering/.venv/lib/python3.9/site-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 5ms/step - accuracy: 0.9283 - loss: 0.2564 - val_accuracy: 0.9729 - val_loss: 0.0852\n",
      "Epoch 2/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9796 - loss: 0.0851 - val_accuracy: 0.9863 - val_loss: 0.0752\n",
      "Epoch 3/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 5ms/step - accuracy: 0.9853 - loss: 0.0754 - val_accuracy: 0.9863 - val_loss: 0.0709\n",
      "Epoch 4/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 6ms/step - accuracy: 0.9853 - loss: 0.0738 - val_accuracy: 0.9864 - val_loss: 0.0695\n",
      "Epoch 5/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 5ms/step - accuracy: 0.9853 - loss: 0.0731 - val_accuracy: 0.9863 - val_loss: 0.0688\n",
      "Epoch 6/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 6ms/step - accuracy: 0.9853 - loss: 0.0727 - val_accuracy: 0.9863 - val_loss: 0.0686\n",
      "Epoch 7/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9853 - loss: 0.0724 - val_accuracy: 0.9863 - val_loss: 0.0688\n",
      "Epoch 8/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9853 - loss: 0.0722 - val_accuracy: 0.9862 - val_loss: 0.0688\n",
      "Epoch 9/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9853 - loss: 0.0719 - val_accuracy: 0.9860 - val_loss: 0.0693\n",
      "Epoch 10/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9853 - loss: 0.0716 - val_accuracy: 0.9859 - val_loss: 0.0697\n",
      "Epoch 11/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9853 - loss: 0.0714 - val_accuracy: 0.9859 - val_loss: 0.0701\n",
      "Epoch 12/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9853 - loss: 0.0712 - val_accuracy: 0.9859 - val_loss: 0.0711\n",
      "Epoch 13/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9853 - loss: 0.0711 - val_accuracy: 0.9859 - val_loss: 0.0717\n",
      "Epoch 14/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9853 - loss: 0.0709 - val_accuracy: 0.9859 - val_loss: 0.0726\n",
      "Epoch 15/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9853 - loss: 0.0708 - val_accuracy: 0.9858 - val_loss: 0.0729\n",
      "Epoch 16/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9853 - loss: 0.0707 - val_accuracy: 0.9858 - val_loss: 0.0729\n",
      "Epoch 17/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9853 - loss: 0.0707 - val_accuracy: 0.9858 - val_loss: 0.0736\n",
      "Epoch 18/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9853 - loss: 0.0707 - val_accuracy: 0.9858 - val_loss: 0.0739\n",
      "Epoch 19/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9853 - loss: 0.0706 - val_accuracy: 0.9858 - val_loss: 0.0738\n",
      "Epoch 20/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9853 - loss: 0.0706 - val_accuracy: 0.9858 - val_loss: 0.0739\n",
      "Epoch 21/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9853 - loss: 0.0706 - val_accuracy: 0.9858 - val_loss: 0.0737\n",
      "Epoch 22/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 6ms/step - accuracy: 0.9853 - loss: 0.0706 - val_accuracy: 0.9858 - val_loss: 0.0742\n",
      "Epoch 23/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9853 - loss: 0.0705 - val_accuracy: 0.9858 - val_loss: 0.0744\n",
      "Epoch 24/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 6ms/step - accuracy: 0.9853 - loss: 0.0705 - val_accuracy: 0.9858 - val_loss: 0.0743\n",
      "Epoch 25/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 5ms/step - accuracy: 0.9853 - loss: 0.0705 - val_accuracy: 0.9858 - val_loss: 0.0743\n",
      "Epoch 26/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 6ms/step - accuracy: 0.9853 - loss: 0.0705 - val_accuracy: 0.9858 - val_loss: 0.0746\n",
      "Epoch 27/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 5ms/step - accuracy: 0.9853 - loss: 0.0705 - val_accuracy: 0.9858 - val_loss: 0.0749\n",
      "Epoch 28/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9853 - loss: 0.0705 - val_accuracy: 0.9858 - val_loss: 0.0748\n",
      "Epoch 29/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 5ms/step - accuracy: 0.9853 - loss: 0.0704 - val_accuracy: 0.9858 - val_loss: 0.0753\n",
      "Epoch 30/30\n",
      "\u001b[1m832/832\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9853 - loss: 0.0704 - val_accuracy: 0.9858 - val_loss: 0.0751\n",
      "\u001b[1m119/119\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step\n",
      "\u001b[1m119/119\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9853 - loss: 0.0684\n",
      "Test loss: 0.06865035742521286\n",
      "Test accuracy: 0.9854958057403564\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m119/119\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for ean_number in ean_numbers:\n",
    "    import pandas as pd\n",
    "\n",
    "\n",
    "    # read data from csv\n",
    "    df = pd.read_csv(f'data_assets_reformatted/all_data_{ean_number}.csv')\n",
    "\n",
    "    # rename columns: timestamp, connected to Date, Value\n",
    "    df.rename(columns={'timestamp': 'Date', 'connected': 'Value'}, inplace=True)\n",
    "\n",
    "    df['Date'] = pd.to_datetime(df['Date'])\n",
    "    df.set_index('Date', inplace=True)\n",
    "\n",
    "\n",
    "    df['Value'] = df['Value'].astype(int)\n",
    "\n",
    "\n",
    "    train_size = int(len(df) * 0.7)\n",
    "    val_size = int(len(df) * 0.2)\n",
    "    test_size = len(df) - train_size - val_size\n",
    "\n",
    "    train, val, test = df.iloc[0:train_size], df.iloc[train_size:train_size+val_size], df.iloc[train_size+val_size:len(df)]\n",
    "\n",
    "\n",
    "    from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "    scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "    train_scaled = scaler.fit_transform(train)\n",
    "    val_scaled = scaler.transform(val)\n",
    "    test_scaled = scaler.transform(test)\n",
    "\n",
    "\n",
    "\n",
    "    import numpy as np\n",
    "\n",
    "    def create_dataset(X, y, time_steps=1):\n",
    "        Xs, ys = [], []\n",
    "        for i in range(len(X) - time_steps):\n",
    "            v = X.iloc[i:(i + time_steps)].values\n",
    "            Xs.append(v)\n",
    "            ys.append(y.iloc[i + time_steps])\n",
    "        return np.array(Xs), np.array(ys)\n",
    "\n",
    "    time_steps = 10\n",
    "\n",
    "    X_train, y_train = create_dataset(train, train.Value, time_steps)\n",
    "    X_val, y_val = create_dataset(val, val.Value, time_steps)\n",
    "    X_test, y_test = create_dataset(test, test.Value, time_steps)\n",
    "\n",
    "\n",
    "\n",
    "    from tensorflow.keras.models import Sequential\n",
    "    from tensorflow.keras.layers import LSTM, Dense\n",
    "\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(128, input_shape=(X_train.shape[1], X_train.shape[2])))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "    history = model.fit(\n",
    "        X_train, y_train,\n",
    "        epochs=30,\n",
    "        batch_size=32,\n",
    "        validation_data=(X_val, y_val),\n",
    "        shuffle=False\n",
    "    )\n",
    "\n",
    "\n",
    "    y_pred = model.predict(X_test)\n",
    "\n",
    "\n",
    "    loss, accuracy = model.evaluate(X_test, y_test)\n",
    "    print('Test loss:', loss)\n",
    "    print('Test accuracy:', accuracy)\n",
    "\n",
    "\n",
    "    import matplotlib.pyplot as plt\n",
    "\n",
    "    plt.figure(figsize=(12, 4))\n",
    "\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.plot(history.history['loss'], label='Train Loss')\n",
    "    plt.plot(history.history['val_loss'], label='Val Loss')\n",
    "    plt.title('Loss')\n",
    "    plt.legend()\n",
    "\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.plot(history.history['accuracy'], label='Train Accuracy')\n",
    "    plt.plot(history.history['val_accuracy'], label='Val Accuracy')\n",
    "    plt.title('Accuracy')\n",
    "    plt.legend()\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "    from sklearn.metrics import classification_report, roc_auc_score\n",
    "\n",
    "    y_pred = model.predict(X_test)\n",
    "\n",
    "    # print(classification_report(y_test, y_pred))\n",
    "\n",
    "    # roc_auc = roc_auc_score(y_test, y_pred)\n",
    "    # print('AUC-ROC:', roc_auc)\n",
    "\n",
    "\n",
    "    # plot y_test and y_pred\n",
    "    plt.figure(figsize=(12, 3))\n",
    "    plt.plot(y_test, label='True')\n",
    "    plt.plot(y_pred, label='Predicted')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    # save the model\n",
    "    model.save(f'models/model_{ean_number}.keras')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}