some kind of pipeline with sklearn?
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5 changed files with 1570 additions and 24 deletions
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.12"
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"version": "3.12.8"
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"varInspector": {
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381
Aufgaben/05 - lineare regression - sklearn.ipynb
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381
Aufgaben/05 - lineare regression - sklearn.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "f19ad4d1",
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"metadata": {},
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"source": [
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"# Lineare Regression mit scikit-learn"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "fca110ae",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"# plotting settings\n",
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"pd.plotting.register_matplotlib_converters()\n",
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"import matplotlib.pyplot as plt\n",
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"%matplotlib inline\n",
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"import seaborn as sns\n",
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"from sklearn.linear_model import LinearRegression"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "6edf6b65",
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"metadata": {},
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"outputs": [
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{
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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>BuildingArea</th>\n",
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" <th>Rooms</th>\n",
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" <th>Price</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>1</th>\n",
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" <td>79.0</td>\n",
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" <td>2</td>\n",
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" <td>1035000.0</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>150.0</td>\n",
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" <td>3</td>\n",
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" <td>1465000.0</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>142.0</td>\n",
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" <td>4</td>\n",
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" <td>1600000.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>6</th>\n",
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" <td>210.0</td>\n",
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" <td>3</td>\n",
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" <td>1876000.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>7</th>\n",
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" <td>107.0</td>\n",
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" <td>2</td>\n",
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" <td>1636000.0</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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" BuildingArea Rooms Price\n",
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"1 79.0 2 1035000.0\n",
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"2 150.0 3 1465000.0\n",
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"4 142.0 4 1600000.0\n",
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"6 210.0 3 1876000.0\n",
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"7 107.0 2 1636000.0"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"melbourne_file_path = 'data/melb_data.csv'\n",
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"melbourne_data = pd.read_csv(melbourne_file_path)\n",
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"melbourne_data = melbourne_data.dropna(axis=0) # entfernen von Daten mit fehlenden Werten\n",
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"# wählen für unser Beispiel einen kleinen Ausschnitt aus den Daten (denselben, wie im ersten Beispiel)\n",
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"max_area = 400\n",
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"max_datapoints = 100\n",
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"data = melbourne_data[melbourne_data['BuildingArea'] < max_area][:max_datapoints][['BuildingArea', 'Rooms', 'Price']]\n",
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"data.head()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7b226e29",
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"metadata": {},
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"source": [
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"Die scikit-learn API erwartet die Trainingsdaten (Inputs, Features) ähnlich zu unserer Feature-Matrix als 2D-Array, allerdings ohne der '1'."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "9e851d19",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([1, 2, 3])"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"r = np.array([1,2,3])\n",
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"r"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "a2903e63",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[1],\n",
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" [2],\n",
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" [3]])"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# ändern eines Vektors der Länge n in eine Matrix der Dimension n x 1:\n",
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"r.reshape((-1,1))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "89d8210c",
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"metadata": {},
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"outputs": [],
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"source": [
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"x = data[['BuildingArea'] ].to_numpy(copy=True).reshape((-1, 1))\n",
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"y = data['Price'].to_numpy(copy=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "b0d5c350",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(100, 1)"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"x.shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "bd1d76e9",
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"w0: 441524.4208318128\n",
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"w1: [6024.22929588]\n"
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]
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}
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],
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"source": [
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"model = LinearRegression().fit(x, y)\n",
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"# print model parameters\n",
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"print('w0: {}'.format(model.intercept_))\n",
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"print('w1: {}'.format(model.coef_))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "12eacd13",
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"metadata": {},
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"source": [
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"Vergleichen Sie die Werte mit der selbstprogrammierten Lösung!"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c5f6e509",
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"metadata": {},
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"source": [
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"**Achtung:** Alle scikit-learn Modelle haben Parameter, die das Verhalten beeinflussen. Mehr in der scikit-learn Dokumentation, z.B. hier für LinearRegression: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "055183fa",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"erklärte Varianz (R^2): 0.22971025499088593\n"
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]
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}
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],
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"source": [
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"r_sq = model.score(x, y)\n",
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"print('erklärte Varianz (R^2): {}'.format(r_sq))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5a66667c",
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"metadata": {},
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"source": [
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"Vorhersage mit trainiertem scikit-learn Modell"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "b81f3f03",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Preis-Vorhersage für ein neues Haus mit Wohnfläche 287: [2170478.22874863]\n"
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]
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}
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],
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"source": [
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"building_area_new_house = [[287]]\n",
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"y_pred = model.predict(building_area_new_house)\n",
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"print('Preis-Vorhersage für ein neues Haus mit Wohnfläche {}: {}'.format(building_area_new_house[0][0], y_pred))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "0d1da016",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Preis-Vorhersagen für Objekte mit den Wohnflächen [[287], [80], [110]]: [2170478.22874863 923462.76450201 1104189.64337833]\n"
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]
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}
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],
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"source": [
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"# Vorhersage für mehrere Objekte auf einmal\n",
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"building_area_new_houses = [[287], [80], [110]]\n",
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"y_preds = model.predict(building_area_new_houses)\n",
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"print('Preis-Vorhersagen für Objekte mit den Wohnflächen {}: {}'.format(building_area_new_houses, y_preds))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5aac9e7a",
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"metadata": {},
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"source": [
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"Alle scikit-learm Modelle haben bestimmte Funktionen, insbesondere `model.fit()` und `model.predict()`, sodass es sehr einfach ist, verschiedene ML-Modelle auszuprobieren."
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]
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},
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{
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"cell_type": "markdown",
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"id": "f39ef84b",
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"metadata": {},
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"source": [
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"**Aufgabe: Mehrere Input-Features ausprobieren!**"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.1"
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},
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"varInspector": {
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"cols": {
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"lenName": 16,
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"lenType": 16,
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"lenVar": 40
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},
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"kernels_config": {
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"python": {
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"delete_cmd_postfix": "",
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"delete_cmd_prefix": "del ",
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"library": "var_list.py",
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"varRefreshCmd": "print(var_dic_list())"
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},
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"r": {
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"delete_cmd_postfix": ") ",
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"delete_cmd_prefix": "rm(",
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"library": "var_list.r",
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"varRefreshCmd": "cat(var_dic_list()) "
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}
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},
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"types_to_exclude": [
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"module",
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"function",
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"builtin_function_or_method",
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"instance",
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"_Feature"
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],
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"window_display": false
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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@ -31,6 +31,7 @@ ipywidgets==8.1.5
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isoduration==20.11.0
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jedi==0.19.2
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Jinja2==3.1.5
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joblib==1.4.2
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json5==0.10.0
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jsonpointer==3.0.0
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jsonschema==4.23.0
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@ -86,6 +87,8 @@ requests==2.32.3
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rfc3339-validator==0.1.4
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rfc3986-validator==0.1.1
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rpds-py==0.22.3
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scikit-learn==1.6.1
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scipy==1.15.1
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seaborn==0.13.2
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Send2Trash==1.8.3
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setuptools==75.8.0
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@ -94,6 +97,7 @@ sniffio==1.3.1
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soupsieve==2.6
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stack-data==0.6.3
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terminado==0.18.1
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threadpoolctl==3.5.0
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tinycss2==1.4.0
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tornado==6.4.2
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tqdm==4.67.1
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1079
tasks/03skykit-regularisierung.ipynb
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1079
tasks/03skykit-regularisierung.ipynb
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