677 lines
20 KiB
Plaintext
677 lines
20 KiB
Plaintext
{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"name": "ium01.ipynb",
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"provenance": [],
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"collapsed_sections": [
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"EYeZaE3Cxf5i"
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],
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"toc_visible": true
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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}
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},
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "zn8GQjYWnbcX"
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},
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"source": [
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"# Notebook for first substask of Inżynieria Uczenia Maszynowego class project.\n",
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"This workbook downloads, normalizes and prints short summary of the dataset I will be working on and its subsets.\n",
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"\n",
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"Link to the dataset at Kaggle.com:\n",
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"\n",
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"https://www.kaggle.com/pcbreviglieri/smart-grid-stability"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "Omh9bzNn7s0Z"
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},
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"source": [
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"#### google colab related stuff"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "Z14xGWuJnWwq",
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"outputId": "250e7323-7b2a-4553-bd10-cd8682c0743f"
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},
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"source": [
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"from google.colab import drive\n",
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"drive.mount('drive')"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"Drive already mounted at drive; to attempt to forcibly remount, call drive.mount(\"drive\", force_remount=True).\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "mROvxIELsVv1"
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},
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"source": [
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"* Click in Colab GUI to allow Colab access and modify Google Drive files"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "hVfCOcburj5P"
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},
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"source": [
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"!mkdir ~/.kaggle\n",
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"!cp drive/MyDrive/kaggle.json ~/.kaggle/.\n",
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"!chmod +x ~/.kaggle/kaggle.json\n",
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"!pip install -q kaggle"
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],
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"execution_count": 1,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "EYeZaE3Cxf5i"
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},
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"source": [
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"# script for lab IUM-01"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "XspjcqV4U9tb"
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},
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"source": [
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"download data"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "3UjQJzTawfKH"
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},
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"source": [
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"!kaggle datasets download -d 'pcbreviglieri/smart-grid-stability' >>/dev/null 2>&1\n",
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"!unzip smart-grid-stability.zip >>/dev/null 2>&1"
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],
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"execution_count": 2,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "mkK6wZ2zmhdQ"
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},
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"source": [
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"read the data as pandas data frame"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "JcPbvjeixwQa"
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},
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"source": [
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"import pandas as pd\n",
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"\n",
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"df = pd.read_csv('smart_grid_stability_augmented.csv')"
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],
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"execution_count": 3,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "x81Ip-6fmnfr"
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},
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"source": [
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"normalize values, so they are all between 0 and 1 (included)"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "7QZX5c2ZMpTj"
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},
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"source": [
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"from sklearn import preprocessing\n",
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"\n",
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"scaler = preprocessing.StandardScaler().fit(df.iloc[:, 0:-1])\n",
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"df_norm_array = scaler.transform(df.iloc[:, 0:-1])\n",
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"df_norm = pd.DataFrame(data=df_norm_array,\n",
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" columns=df.columns[:-1])\n",
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"df_norm['stabf'] = df['stabf']"
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],
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"execution_count": 4,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "hjAT_K-Cmzhq"
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},
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"source": [
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"divide the data into train, test and validation subsets"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "MvI7kiL0UPc8"
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},
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"source": [
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"from sklearn.model_selection import train_test_split\n",
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"\n",
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"df_norm_data = df_norm.copy()\n",
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"df_norm_data = df_norm_data.drop('stab', axis=1)\n",
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"df_norm_labels = df_norm_data.pop('stabf')\n",
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"\n",
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"X_train, X_testAndValid, Y_train, Y_testAndValid = train_test_split(\n",
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" df_norm_data,\n",
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" df_norm_labels,\n",
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" test_size=0.2,\n",
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" random_state=42)\n",
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"\n",
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"X_test, X_valid, Y_test, Y_valid = train_test_split(\n",
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" X_testAndValid,\n",
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" Y_testAndValid,\n",
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" test_size=0.5,\n",
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" random_state=42)"
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],
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"execution_count": 5,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "bM2HBhhlyU3Q"
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},
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"source": [
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"train = pd.concat([X_train, Y_train], axis=1)\n",
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"test = pd.concat([X_test, Y_test], axis=1)\n",
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"valid = pd.concat([X_valid, Y_valid], axis=1)"
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],
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"execution_count": 6,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "FdUL87MgnE2G"
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},
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"source": [
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"print short summary of the dataset and its subsets"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "WUrX63SGcHSB"
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},
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"source": [
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"def namestr(obj, namespace):\n",
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" return [name for name in namespace if namespace[name] is obj]\n",
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"\n",
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"dataset = df_norm\n",
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"for x in [dataset, train, test, valid]:\n",
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" print([q for q in namestr(x, globals()) if len(q) == max([len(w) for w in namestr(x, globals())])][-1]) \n",
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" print(\"size:\", len(x))\n",
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" print(x.describe(include='all'))\n",
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" print(\"class distribution\", x.value_counts('stabf'))\n",
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" print('===============================================================')"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "66jPkCNlVGK9"
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},
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"source": [
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"# script for lab IUM-03"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "SRF-igrsma-A"
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},
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"source": [
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"download data"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "IkZTO5PhVB7R"
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},
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"source": [
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"!kaggle datasets download -d 'pcbreviglieri/smart-grid-stability' >>/dev/null 2>&1\n",
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"!unzip smart-grid-stability.zip >>/dev/null 2>&1"
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],
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "ijEeDXgpLYqk"
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},
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"source": [
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"check how many data entries is in the dataset"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "p9ONnUUIW27z",
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"outputId": "cb078dbb-daa0-4ae6-ec08-9b84141c8507"
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},
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"source": [
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"!wc -l smart_grid_stability_augmented.csv"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"60001 smart_grid_stability_augmented.csv\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "jQw4a7ZjMmXr"
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},
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"source": [
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"take a look at the dataset to choose columns to keep"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 226
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},
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"id": "82msg0JoVZiK",
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"outputId": "9c0243dd-f21d-4453-dd56-7410c4391cdd"
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},
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"source": [
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"import pandas as pd\n",
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"df = pd.read_csv('smart_grid_stability_augmented.csv')\n",
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"df.head()"
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],
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"execution_count": null,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>tau1</th>\n",
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" <th>tau2</th>\n",
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" <th>tau3</th>\n",
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" <th>tau4</th>\n",
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" <th>p1</th>\n",
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" <th>p2</th>\n",
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" <th>p3</th>\n",
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" <th>p4</th>\n",
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" <th>g1</th>\n",
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" <th>g2</th>\n",
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" <th>g3</th>\n",
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" <th>g4</th>\n",
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" <th>stab</th>\n",
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" <th>stabf</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>2.959060</td>\n",
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" <td>3.079885</td>\n",
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" <td>8.381025</td>\n",
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" <td>9.780754</td>\n",
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" <td>3.763085</td>\n",
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" <td>-0.782604</td>\n",
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" <td>-1.257395</td>\n",
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" <td>-1.723086</td>\n",
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" <td>0.650456</td>\n",
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" <td>0.859578</td>\n",
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" <td>0.887445</td>\n",
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" <td>0.958034</td>\n",
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" <td>0.055347</td>\n",
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" <td>unstable</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>9.304097</td>\n",
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" <td>4.902524</td>\n",
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" <td>3.047541</td>\n",
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" <td>1.369357</td>\n",
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" <td>5.067812</td>\n",
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" <td>-1.940058</td>\n",
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" <td>-1.872742</td>\n",
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" <td>-1.255012</td>\n",
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" <td>0.413441</td>\n",
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" <td>0.862414</td>\n",
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" <td>0.562139</td>\n",
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" <td>0.781760</td>\n",
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" <td>-0.005957</td>\n",
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" <td>stable</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>8.971707</td>\n",
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" <td>8.848428</td>\n",
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" <td>3.046479</td>\n",
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" <td>1.214518</td>\n",
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" <td>3.405158</td>\n",
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" <td>-1.207456</td>\n",
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" <td>-1.277210</td>\n",
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" <td>-0.920492</td>\n",
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" <td>0.163041</td>\n",
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" <td>0.766689</td>\n",
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" <td>0.839444</td>\n",
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" <td>0.109853</td>\n",
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" <td>0.003471</td>\n",
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" <td>unstable</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>0.716415</td>\n",
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" <td>7.669600</td>\n",
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" <td>4.486641</td>\n",
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" <td>2.340563</td>\n",
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" <td>3.963791</td>\n",
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" <td>-1.027473</td>\n",
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" <td>-1.938944</td>\n",
|
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" <td>-0.997374</td>\n",
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" <td>0.446209</td>\n",
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" <td>0.976744</td>\n",
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" <td>0.929381</td>\n",
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" <td>0.362718</td>\n",
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" <td>0.028871</td>\n",
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" <td>unstable</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>3.134112</td>\n",
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" <td>7.608772</td>\n",
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" <td>4.943759</td>\n",
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" <td>9.857573</td>\n",
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" <td>3.525811</td>\n",
|
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" <td>-1.125531</td>\n",
|
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" <td>-1.845975</td>\n",
|
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" <td>-0.554305</td>\n",
|
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" <td>0.797110</td>\n",
|
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" <td>0.455450</td>\n",
|
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" <td>0.656947</td>\n",
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" <td>0.820923</td>\n",
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" <td>0.049860</td>\n",
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" <td>unstable</td>\n",
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" </tr>\n",
|
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" tau1 tau2 tau3 ... g4 stab stabf\n",
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"0 2.959060 3.079885 8.381025 ... 0.958034 0.055347 unstable\n",
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"1 9.304097 4.902524 3.047541 ... 0.781760 -0.005957 stable\n",
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"2 8.971707 8.848428 3.046479 ... 0.109853 0.003471 unstable\n",
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"3 0.716415 7.669600 4.486641 ... 0.362718 0.028871 unstable\n",
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"4 3.134112 7.608772 4.943759 ... 0.820923 0.049860 unstable\n",
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"\n",
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"[5 rows x 14 columns]"
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]
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},
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|
"metadata": {
|
|
"tags": []
|
|
},
|
|
"execution_count": 5
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|
}
|
|
]
|
|
},
|
|
{
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|
"cell_type": "markdown",
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|
"metadata": {
|
|
"id": "KHBmXrjALz0W"
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|
},
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"source": [
|
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"discard some of the columns; shuffle the data; divide into train, test and validations subsets and print number of rows of the subsets"
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]
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},
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|
{
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"cell_type": "code",
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "tJo2DytZVyOo",
|
|
"outputId": "e61890d6-3f2f-4008-a83d-b7272aeecd11"
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|
},
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"source": [
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"!sed 1d smart_grid_stability_augmented.csv | cut -f 1,5,9,13,14 -d \",\" | shuf | split -l 48000\n",
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"!mv xaa train.csv\n",
|
|
"!mv xab toDivide\n",
|
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"!split -l 6000 toDivide\n",
|
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"!mv xaa test.csv\n",
|
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"!mv xab valid.csv\n",
|
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"!wc -l train.csv\n",
|
|
"!wc -l test.csv\n",
|
|
"!wc -l valid.csv\n"
|
|
],
|
|
"execution_count": null,
|
|
"outputs": [
|
|
{
|
|
"output_type": "stream",
|
|
"text": [
|
|
"48000 train.csv\n",
|
|
"6000 test.csv\n",
|
|
"6000 valid.csv\n"
|
|
],
|
|
"name": "stdout"
|
|
}
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"id": "HhVyLU0XIZgt"
|
|
},
|
|
"source": [
|
|
"# script for lab IUM-05 - Model and training\n",
|
|
"\n",
|
|
"### first run lab IUM-01!!!"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"metadata": {
|
|
"id": "dGX3WRlPIQYO"
|
|
},
|
|
"source": [
|
|
"import tensorflow as tf\n",
|
|
"from tensorflow.keras import layers\n",
|
|
"\n",
|
|
"model = tf.keras.Sequential([\n",
|
|
" layers.Input(shape=(12,)),\n",
|
|
" layers.Dense(32),\n",
|
|
" layers.Dense(16),\n",
|
|
" layers.Dense(2, activation='softmax')\n",
|
|
"])\n",
|
|
"\n",
|
|
"model.compile(\n",
|
|
" loss=tf.losses.BinaryCrossentropy(),\n",
|
|
" optimizer=tf.optimizers.Adam(),\n",
|
|
" metrics=[tf.keras.metrics.BinaryAccuracy()])"
|
|
],
|
|
"execution_count": 32,
|
|
"outputs": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"metadata": {
|
|
"id": "l6H4gchZqpBM"
|
|
},
|
|
"source": [
|
|
"import numpy as np\n",
|
|
"\n",
|
|
"def onezero(label):\n",
|
|
" return 0 if label == 'unstable' else 1\n",
|
|
"\n",
|
|
"\n",
|
|
"Y_train_one_zero = [onezero(x) for x in Y_train]\n",
|
|
"Y_train_onehot = np.eye(2)[Y_train_one_zero]\n",
|
|
"\n",
|
|
"Y_test_one_zero = [onezero(x) for x in Y_test]\n",
|
|
"Y_test_onehot = np.eye(2)[Y_test_one_zero]\n"
|
|
],
|
|
"execution_count": 39,
|
|
"outputs": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "ZJnjZvMOqcxr",
|
|
"outputId": "119a86a5-d9de-41f2-9ff7-c4576aec11ab"
|
|
},
|
|
"source": [
|
|
"history = model.fit(tf.convert_to_tensor(X_train, np.float32),\n",
|
|
" Y_train_onehot, epochs=5)"
|
|
],
|
|
"execution_count": 36,
|
|
"outputs": [
|
|
{
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Epoch 1/5\n",
|
|
"1500/1500 [==============================] - 2s 1ms/step - loss: 0.3926 - binary_accuracy: 0.8149\n",
|
|
"Epoch 2/5\n",
|
|
"1500/1500 [==============================] - 2s 1ms/step - loss: 0.3925 - binary_accuracy: 0.8161\n",
|
|
"Epoch 3/5\n",
|
|
"1500/1500 [==============================] - 2s 1ms/step - loss: 0.3922 - binary_accuracy: 0.8150\n",
|
|
"Epoch 4/5\n",
|
|
"1500/1500 [==============================] - 2s 1ms/step - loss: 0.3927 - binary_accuracy: 0.8146\n",
|
|
"Epoch 5/5\n",
|
|
"1500/1500 [==============================] - 2s 1ms/step - loss: 0.3926 - binary_accuracy: 0.8143\n"
|
|
],
|
|
"name": "stdout"
|
|
}
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "a-TyrAertSsr",
|
|
"outputId": "20f2996b-b2c8-494d-8e1b-c612acb2ede0"
|
|
},
|
|
"source": [
|
|
"model.summary()"
|
|
],
|
|
"execution_count": 35,
|
|
"outputs": [
|
|
{
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Model: \"sequential_6\"\n",
|
|
"_________________________________________________________________\n",
|
|
"Layer (type) Output Shape Param # \n",
|
|
"=================================================================\n",
|
|
"dense_28 (Dense) (None, 32) 416 \n",
|
|
"_________________________________________________________________\n",
|
|
"dense_29 (Dense) (None, 16) 528 \n",
|
|
"_________________________________________________________________\n",
|
|
"dense_30 (Dense) (None, 2) 34 \n",
|
|
"=================================================================\n",
|
|
"Total params: 978\n",
|
|
"Trainable params: 978\n",
|
|
"Non-trainable params: 0\n",
|
|
"_________________________________________________________________\n"
|
|
],
|
|
"name": "stdout"
|
|
}
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"metadata": {
|
|
"colab": {
|
|
"base_uri": "https://localhost:8080/"
|
|
},
|
|
"id": "tBzh-0yLWV1P",
|
|
"outputId": "8886d20a-5a2f-4385-bb9d-a033d4f4d50b"
|
|
},
|
|
"source": [
|
|
"results = model.evaluate(X_test, Y_test_onehot, batch_size=64)\n",
|
|
"print('test loss: ',results[0])\n",
|
|
"print('test acc: ', results[1])"
|
|
],
|
|
"execution_count": 41,
|
|
"outputs": [
|
|
{
|
|
"output_type": "stream",
|
|
"text": [
|
|
"94/94 [==============================] - 0s 1ms/step - loss: 0.3933 - binary_accuracy: 0.8112\n",
|
|
"test loss: 0.3933383822441101\n",
|
|
"test acc: 0.8111666440963745\n"
|
|
],
|
|
"name": "stdout"
|
|
}
|
|
]
|
|
}
|
|
]
|
|
} |