{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "fv9OoQsMFk5A" }, "source": [ "# Ajasarja prognoosimine, kasutades toetavate vektorite regressorit\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Selles märkmikus demonstreerime, kuidas:\n", "\n", "- ette valmistada 2D aegridade andmeid SVM regressioonimudeli treenimiseks\n", "- rakendada SVR-i, kasutades RBF-tuumat\n", "- hinnata mudelit graafikute ja MAPE abil\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Moodulite importimine\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": [ "## Andmete ettevalmistamine\n" ] }, { "cell_type": "markdown", "metadata": { "id": "8fywSjC6GsRz" }, "source": [ "### Laadi andmed\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": [ "
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load
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" ], "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": [ "### Joonista andmed\n" ] }, { "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": [ "### Loo treening- ja testandmed\n" ] }, { "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": [ "### Andmete ettevalmistamine treenimiseks\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nüüd peate andmed treenimiseks ette valmistama, tehes andmete filtreerimise ja skaleerimise.\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": [ "Skaleeri andmed vahemikku (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": [ "### Andmete loomine ajasammudega\n" ] }, { "cell_type": "markdown", "metadata": { "id": "fdmxTZtOQ8xs" }, "source": [ "Meie SVR-i jaoks teisendame sisendandmed kujule `[batch, timesteps]`. Seega muudame olemasoleva `train_data` ja `test_data` kuju nii, et lisandub uus dimensioon, mis viitab ajastustele. Meie näites võtame `timesteps = 5`. Seega on mudeli sisendiks andmed esimese 4 ajastuse kohta ja väljundiks on andmed 5nda ajastuse kohta.\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": [ "## SVR mudeli loomine\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": [ "### Tee mudeli ennustus\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": [ "## Mudeli jõudluse analüüs\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": [ "## Kogu andmestiku ennustus\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**Lahtiütlus**: \nSee dokument on tõlgitud AI tõlketeenuse [Co-op Translator](https://github.com/Azure/co-op-translator) abil. Kuigi püüame tagada täpsust, palume arvestada, et automaatsed tõlked võivad sisaldada vigu või ebatäpsusi. Algne dokument selle algses keeles tuleks pidada autoriteetseks allikaks. Olulise teabe puhul soovitame kasutada professionaalset inimtõlget. Me ei vastuta selle tõlke kasutamisest tulenevate arusaamatuste või valesti tõlgenduste eest.\n" ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [], "name": "Recurrent_Neural_Networks.ipynb", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "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": "3.7.1" }, "coopTranslator": { "original_hash": "e86ce102239a14c44585623b9b924a74", "translation_date": "2025-10-11T12:38:28+00:00", "source_file": "7-TimeSeries/3-SVR/working/notebook.ipynb", "language_code": "et" } }, "nbformat": 4, "nbformat_minor": 1 }