{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "fv9OoQsMFk5A" }, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ebben a jegyzetfüzetben bemutatjuk, hogyan kell:\n", "\n", "- 2D időbeli adatsorokat előkészíteni egy SVM regresszor modell betanításához\n", "- SVR-t megvalósítani RBF kernel használatával\n", "- a modellt értékelni grafikonok és MAPE segítségével\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Modulok importálása\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": [ "## Adatok előkészítése\n" ] }, { "cell_type": "markdown", "metadata": { "id": "8fywSjC6GsRz" }, "source": [ "### Adatok betöltése\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": [] }, { "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": [ "Most már elő kell készítened az adatokat a tanításhoz az adatok szűrésével és skálázásával.\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": [ "Skálázza az adatokat a (0, 1) tartományba.\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": [ "### Adatok létrehozása időlépésekkel\n" ] }, { "cell_type": "markdown", "metadata": { "id": "fdmxTZtOQ8xs" }, "source": [ "SVR esetén az input adatokat `[batch, timesteps]` formára alakítjuk. Ezért az `train_data` és `test_data` meglévő adatait úgy alakítjuk át, hogy egy új dimenziót hozzunk létre, amely a timesteps-re utal. Példánkban `timesteps = 5` értéket veszünk. Tehát a modell bemenete az első 4 timestep adata lesz, a kimenet pedig az 5. timestep adata.\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": [ "### Készítsen modell előrejelzést\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": [ "## Teljes adathalmaz előrejelzés\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**Felelősségkizárás**: \nEz a dokumentum az [Co-op Translator](https://github.com/Azure/co-op-translator) AI fordítási szolgáltatás segítségével készült. Bár törekszünk a pontosságra, kérjük, vegye figyelembe, hogy az automatikus fordítások hibákat vagy pontatlanságokat tartalmazhatnak. Az eredeti dokumentum az eredeti nyelvén tekintendő hiteles forrásnak. Kritikus információk esetén javasolt a professzionális, emberi fordítás igénybevétele. Nem vállalunk felelősséget a fordítás használatából eredő félreértésekért vagy téves értelmezésekért.\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": "2025-09-04T07:41:16+00:00", "source_file": "7-TimeSeries/3-SVR/working/notebook.ipynb", "language_code": "hu" } }, "nbformat": 4, "nbformat_minor": 1 }