PythonDataScience-Loesungsvorschlag.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Data Science in Python\n",
    "## am Beispiel *California Housing*\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "% pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Laden der Daten (Trainings- und Validierungsdatensatz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from utilities import load_data\n",
    "Xtrain, ytrain, Xtest, ytest = load_data()\n",
    "Xtrain.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Plotten der Zielverteilung"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "plt.hist(ytrain, bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Trivialmodell: Mittelwert"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from utilities import evaluate\n",
    "trivialprognose = np.mean(ytrain)\n",
    "evaluate(trivialprognose, ytest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Erstes Modell: Lineare Regression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "est = LinearRegression()\n",
    "\n",
    "est.fit(Xtrain, ytrain)\n",
    "prediction = est.predict(Xtest)\n",
    "\n",
    "evaluate(prediction, ytest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Zweites Modell: Support Vektor Regression\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import SVR\n",
    "\n",
    "est = SVR(max_iter=5000)\n",
    "est.fit(Xtrain, ytrain)\n",
    "\n",
    "prediction = est.predict(Xtest)\n",
    "evaluate(prediction, ytest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Erweiterung mit Skalierungsverfahren\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "pipe = Pipeline([\n",
    "        ('scaler', StandardScaler()),\n",
    "        ('regressor', SVR(max_iter=5000))\n",
    "    ])\n",
    "pipe.fit(Xtrain, ytrain)\n",
    "\n",
    "prediction = pipe.predict(Xtest)\n",
    "evaluate(prediction, ytest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Weitere Betrachtung Zweidimensionale Korrelationen\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "Xtrain, ytrain, Xtest, ytest = load_data()\n",
    "\n",
    "from utilities import visualize\n",
    "visualize(ytest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Erweiterung: Separate Regression der Geokoordinaten\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "from utilities import RegressionOnSubset\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "Xtrain, ytrain, Xtest, ytest = load_data()\n",
    "\n",
    "columns = [\"Longitude\", \"Latitude\"]\n",
    "\n",
    "pipe = Pipeline([\n",
    "         ('geo_regressor', RegressionOnSubset(\n",
    "            KNeighborsRegressor(), columns)),\n",
    "        ('scaler', StandardScaler()),\n",
    "        ('regressor', SVR(max_iter=5000)),\n",
    "    ])\n",
    "\n",
    "pipe.fit(Xtrain, ytrain)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Visualisieren der Ergebnisse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "Xtrain, ytrain, Xtest, ytest = load_data()\n",
    "prediction = pipe.predict(Xtest)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "Xtest.head()\n",
    "visualize(Xtest.knearest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "evaluate(prediction, ytest)"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}