{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fv9OoQsMFk5A"
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I denne notebook demonstrerer vi, hvordan man:\n",
    "\n",
    "- forbereder 2D-tidsseriedata til træning af en SVM-regressormodel  \n",
    "- implementerer SVR ved hjælp af RBF-kernen  \n",
    "- evaluerer modellen ved hjælp af grafer og MAPE  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importerer moduler\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('../../')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "M687KNlQFp0-"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "import math\n",
    "\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from common.utils import load_data, mape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Cj-kfVdMGjWP"
   },
   "source": [
    "## Forbereder data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8fywSjC6GsRz"
   },
   "source": [
    "### Indlæs data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "id": "aBDkEB11Fumg",
    "outputId": "99cf7987-0509-4b73-8cc2-75d7da0d2740"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01 00:00:00</th>\n",
       "      <td>2698.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 01:00:00</th>\n",
       "      <td>2558.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 02:00:00</th>\n",
       "      <td>2444.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 03:00:00</th>\n",
       "      <td>2402.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 04:00:00</th>\n",
       "      <td>2403.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       load\n",
       "2012-01-01 00:00:00  2698.0\n",
       "2012-01-01 01:00:00  2558.0\n",
       "2012-01-01 02:00:00  2444.0\n",
       "2012-01-01 03:00:00  2402.0\n",
       "2012-01-01 04:00:00  2403.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "energy = load_data('../../data')[['load']]\n",
    "energy.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "O0BWP13rGnh4"
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 486
    },
    "id": "hGaNPKu_Gidk",
    "outputId": "7f89b326-9057-4f49-efbe-cb100ebdf76d"
   },
   "outputs": [],
   "source": [
    "energy.plot(y='load', subplots=True, figsize=(15, 8), fontsize=12)\n",
    "plt.xlabel('timestamp', fontsize=12)\n",
    "plt.ylabel('load', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IPuNor4eGwYY"
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ysvsNyONGt0Q"
   },
   "outputs": [],
   "source": [
    "train_start_dt = '2014-11-01 00:00:00'\n",
    "test_start_dt = '2014-12-30 00:00:00'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 548
    },
    "id": "SsfdLoPyGy9w",
    "outputId": "d6d6c25b-b1f4-47e5-91d1-707e043237d7"
   },
   "outputs": [],
   "source": [
    "energy[(energy.index < test_start_dt) & (energy.index >= train_start_dt)][['load']].rename(columns={'load':'train'}) \\\n",
    "    .join(energy[test_start_dt:][['load']].rename(columns={'load':'test'}), how='outer') \\\n",
    "    .plot(y=['train', 'test'], figsize=(15, 8), fontsize=12)\n",
    "plt.xlabel('timestamp', fontsize=12)\n",
    "plt.ylabel('load', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XbFTqBw6G1Ch"
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nu skal du forberede dataene til træning ved at udføre filtrering og skalering af dine data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cYivRdQpHDj3",
    "outputId": "a138f746-461c-4fd6-bfa6-0cee094c4aa1"
   },
   "outputs": [],
   "source": [
    "train = energy.copy()[(energy.index >= train_start_dt) & (energy.index < test_start_dt)][['load']]\n",
    "test = energy.copy()[energy.index >= test_start_dt][['load']]\n",
    "\n",
    "print('Training data shape: ', train.shape)\n",
    "print('Test data shape: ', test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Skaler dataene til at være i området (0, 1).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "id": "3DNntGQnZX8G",
    "outputId": "210046bc-7a66-4ccd-d70d-aa4a7309949c"
   },
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()\n",
    "train['load'] = scaler.fit_transform(train)\n",
    "train.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "26Yht-rzZexe",
    "outputId": "20326077-a38a-4e78-cc5b-6fd7af95d301"
   },
   "outputs": [],
   "source": [
    "test['load'] = scaler.transform(test)\n",
    "test.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x0n6jqxOQ41Z"
   },
   "source": [
    "### Oprettelse af data med tidsintervaller\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fdmxTZtOQ8xs"
   },
   "source": [
    "For vores SVR transformerer vi inputdataene til at være i formen `[batch, timesteps]`. Så vi omformer de eksisterende `train_data` og `test_data`, så der er en ny dimension, der refererer til timesteps. I vores eksempel tager vi `timesteps = 5`. Så input til modellen er dataene for de første 4 timesteps, og output vil være dataene for den 5. timestep.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Rpju-Sc2HFm0"
   },
   "outputs": [],
   "source": [
    "# Converting to numpy arrays\n",
    "\n",
    "train_data = train.values\n",
    "test_data = test.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecting the timesteps\n",
    "\n",
    "timesteps=None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "O-JrsrsVJhUQ",
    "outputId": "c90dbe71-bacc-4ec4-b452-f82fe5aefaef"
   },
   "outputs": [],
   "source": [
    "# Converting data to 2D tensor\n",
    "\n",
    "train_data_timesteps=None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "exJD8AI7KE4g",
    "outputId": "ce90260c-f327-427d-80f2-77307b5a6318"
   },
   "outputs": [],
   "source": [
    "# Converting test data to 2D tensor\n",
    "\n",
    "test_data_timesteps=None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "2u0R2sIsLuq5"
   },
   "outputs": [],
   "source": [
    "x_train, y_train = None\n",
    "x_test, y_test = None\n",
    "\n",
    "print(x_train.shape, y_train.shape)\n",
    "print(x_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8wIPOtAGLZlh"
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "EhA403BEPEiD"
   },
   "outputs": [],
   "source": [
    "# Create model using RBF kernel\n",
    "\n",
    "model = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GS0UA3csMbqp",
    "outputId": "d86b6f05-5742-4c1d-c2db-c40510bd4f0d"
   },
   "outputs": [],
   "source": [
    "# Fit model on training data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Rz_x8S3UrlcF"
   },
   "source": [
    "### Lav modelprediktion\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XR0gnt3MnuYS",
    "outputId": "157e40ab-9a23-4b66-a885-0d52a24b2364"
   },
   "outputs": [],
   "source": [
    "# Making predictions\n",
    "\n",
    "y_train_pred = None\n",
    "y_test_pred = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_2epncg-SGzr"
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scaling the predictions\n",
    "\n",
    "y_train_pred = scaler.inverse_transform(y_train_pred)\n",
    "y_test_pred = scaler.inverse_transform(y_test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xmm_YLXhq7gV",
    "outputId": "18392f64-4029-49ac-c71a-a4e2411152a1"
   },
   "outputs": [],
   "source": [
    "# Scaling the original values\n",
    "\n",
    "y_train = scaler.inverse_transform(y_train)\n",
    "y_test = scaler.inverse_transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "u3LBj93coHEi",
    "outputId": "d4fd49e8-8c6e-4bb0-8ef9-ca0b26d725b4"
   },
   "outputs": [],
   "source": [
    "# Extract the timesteps for x-axis\n",
    "\n",
    "train_timestamps = None\n",
    "test_timestamps = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(25,6))\n",
    "# plot original output\n",
    "# plot predicted output\n",
    "plt.legend(['Actual','Predicted'])\n",
    "plt.xlabel('Timestamp')\n",
    "plt.title(\"Training data prediction\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LnhzcnYtXHCm",
    "outputId": "f5f0d711-f18b-4788-ad21-d4470ea2c02b"
   },
   "outputs": [],
   "source": [
    "print('MAPE for training data: ', mape(y_train_pred, y_train)*100, '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 225
    },
    "id": "53Q02FoqQH4V",
    "outputId": "53e2d59b-5075-4765-ad9e-aed56c966583"
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10,3))\n",
    "# plot original output\n",
    "# plot predicted output\n",
    "plt.legend(['Actual','Predicted'])\n",
    "plt.xlabel('Timestamp')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "clOAUH-SXCJG",
    "outputId": "a3aa85ff-126a-4a4a-cd9e-90b9cc465ef5"
   },
   "outputs": [],
   "source": [
    "print('MAPE for testing data: ', mape(y_test_pred, y_test)*100, '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DHlKvVCId5ue"
   },
   "source": [
    "## Fuld datasæt forudsigelse\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cOFJ45vreO0N",
    "outputId": "35628e33-ecf9-4966-8036-f7ea86db6f16"
   },
   "outputs": [],
   "source": [
    "# Extracting load values as numpy array\n",
    "data = None\n",
    "\n",
    "# Scaling\n",
    "data = None\n",
    "\n",
    "# Transforming to 2D tensor as per model input requirement\n",
    "data_timesteps=None\n",
    "\n",
    "# Selecting inputs and outputs from data\n",
    "X, Y = None, None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ESSAdQgwexIi"
   },
   "outputs": [],
   "source": [
    "# Make model predictions\n",
    "\n",
    "# Inverse scale and reshape\n",
    "Y_pred = None\n",
    "Y = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 328
    },
    "id": "M_qhihN0RVVX",
    "outputId": "a89cb23e-1d35-437f-9d63-8b8907e12f80"
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(30,8))\n",
    "# plot original output\n",
    "# plot predicted output\n",
    "plt.legend(['Actual','Predicted'])\n",
    "plt.xlabel('Timestamp')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AcN7pMYXVGTK",
    "outputId": "7e1c2161-47ce-496c-9d86-7ad9ae0df770"
   },
   "outputs": [],
   "source": [
    "print('MAPE: ', mape(Y_pred, Y)*100, '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n---\n\n**Ansvarsfraskrivelse**:  \nDette dokument er blevet oversat ved hjælp af AI-oversættelsestjenesten [Co-op Translator](https://github.com/Azure/co-op-translator). Selvom vi bestræber os på at sikre nøjagtighed, skal du være opmærksom på, at automatiserede oversættelser kan indeholde fejl eller unøjagtigheder. Det originale dokument på dets oprindelige sprog bør betragtes som den autoritative kilde. For kritisk information anbefales professionel menneskelig oversættelse. Vi påtager os intet ansvar for misforståelser eller fejltolkninger, der måtte opstå som følge af brugen af denne oversættelse.\n"
   ]
  }
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