{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fv9OoQsMFk5A"
   },
   "source": [
    "# Napovedovanje časovnih vrst z uporabo regresorja podpornih vektorjev\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "V tem zvezku bomo prikazali, kako:\n",
    "\n",
    "- pripraviti 2D časovne vrste podatkov za učenje modela SVM regresorja\n",
    "- implementirati SVR z uporabo RBF jedra\n",
    "- oceniti model s pomočjo grafov in MAPE\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Uvažanje modulov\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": [
    "## Priprava podatkov\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8fywSjC6GsRz"
   },
   "source": [
    "### Naloži podatke\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",
       "        vertical-align: middle;\n",
       "    }\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": [
    "Zdaj morate pripraviti podatke za učenje z izvajanjem filtriranja in skaliranja vaših podatkov.\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": [
    "Prilagodite podatke, da bodo v razponu (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": [
    "### Ustvarjanje podatkov s časovnimi koraki\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fdmxTZtOQ8xs"
   },
   "source": [
    "Za naš SVR preoblikujemo vhodne podatke v obliko `[batch, timesteps]`. Tako preoblikujemo obstoječe `train_data` in `test_data`, da dodamo novo dimenzijo, ki se nanaša na časovne korake. V našem primeru vzamemo `timesteps = 5`. Tako so vhodi v model podatki za prve 4 časovne korake, izhod pa bodo podatki za 5. časovni korak.\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": [
    "## Ustvarjanje SVR modela\n"
   ]
  },
  {
   "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": [
    "### Ustvari napoved modela\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": [
    "## Analiza učinkovitosti modela\n"
   ]
  },
  {
   "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": [
    "## Napoved celotnega nabora podatkov\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**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za strojno prevajanje [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da upoštevate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo strokovno človeško prevajanje. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki izhajajo iz uporabe tega prevoda.\n"
   ]
  }
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