{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Anwendungsbeispiel: Ridge Regression vs. SVM\n", "

\n", " \n", "
\n", "In diesem Anwendungsbeispiel vergleichen wir Ridge Regression mit einer C-SVM. Die Daten wurden künstlich erstellt und dienen nur als Veranschaulichung einer potentiellen echten Anwendung.\n", "
\n", "\n", "## Erläuterung der (Toy-)Anwendung \n", "

\n", " \n", "
\n", "Ein neues Medikament kommt auf den Markt, welches sich gut für die Therapie eines speziellen Tumors eignet. Jedoch hat man festgestellt, dass es bei einer Gruppe von Patienten schneller wirkt als bei einer anderen. Leider kommt es bei einer längeren Behandlung mit dem Medikament zu erheblichen Nebenwirkungen. Daher möchte man vermeiden, dass Patienten das Medikament verabreicht wird, wenn es eine zu lange Reaktionszeit benötigen würde. \n", "
\n", "
\n", "
\n", "Es wird vermutet, dass verschiedene genetische Veränderungen (Mutationen) im Genom der einzelnen Patienten eine Rolle spielen könnten, weshalb das Medikament eine schnellere oder längere Reaktionszeit hat. \n", "
\n", "
\n", "
\n", "Unsere Aufgabe als Machine Learner ist es nun, ein Vorhersagemodell zu entwickeln, welches uns anhand der genetischen Unterschiede zwischen den Patienten die Reaktionszeit (Anzahl der Tage) vorhersagt, ab wann das Medikament für den individuellen Patienten zu wirken beginnt. \n", "
\n", "
\n", "
\n", "Um die Reaktionszeit in Tagen vorherzusagen, bekommen wir von unserem Auftragssteller einen Datensatz von 400 Patienten. Für jeden Patienten wurden 600 verschiedene genetische Veränderungen (Mutationen) an derselben Stelle im Genom gemessen. Zusätzlich wurde die Anzahl der Tage notiert, ab wann das Medikament für den individuellen Patient begonnen hat zu wirken.\n", "
\n", "\n", "\n", "## 1. Vorhersage der genauen Reaktionszeit mit Ridge Regression\n", "
\n", "Um die Reaktionszeit des Medikaments für neue Patienten vorherzusagen, verwenden wir Ridge Regression, da es sich um ein Regressionproblem handelt und wir mehr Features als Patienten haben. Zudem verwenden wir Ridge Regression, um ein Modell zu lernen, welches nicht überangepasst ist (overfitted) und gut auf unbekannte Patienten generalisiert, indem wir den optimalen Regularisierungsparameter lernen.\n", "
\n", "### 1.1 Daten Vorverarbeitung" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Orginal Daten\n", "Anzahl Patienten:\t400\n", "Anzahl Features:\t600\n", "\n", "Trainingsdaten\n", "Anzahl Patienten:\t320\n", "Anzahl Features:\t600\n", "\n", "Testdaten\n", "Anzahl Patienten:\t80\n", "Anzahl Features:\t600\n" ] } ], "source": [ "%matplotlib inline\n", "import scipy as sp\n", "import matplotlib\n", "import pylab as pl\n", "matplotlib.rcParams.update({'font.size': 15})\n", "\n", "from sklearn.linear_model import Ridge\n", "from sklearn.svm import SVC\n", "from sklearn.model_selection import KFold, StratifiedKFold, GridSearchCV,StratifiedShuffleSplit\n", "from sklearn.model_selection import cross_val_score, train_test_split\n", "from sklearn.metrics import accuracy_score, mean_squared_error, mean_absolute_error\n", "\n", "random_state = 42\n", "\n", "#Lade Simulierte Daten\n", "data = sp.loadtxt(\"toydata/X.txt\")\n", "binary_target = sp.loadtxt(\"toydata/y_binary.txt\")\n", "continuous_target = sp.loadtxt(\"toydata/y.txt\")\n", "\n", "#Zusammenfassung der Daten\n", "print(\"Orginal Daten\")\n", "print(\"Anzahl Patienten:\\t%d\"%data.shape[0])\n", "print(\"Anzahl Features:\\t%d\"%data.shape[1])\n", "print\n", "\n", "#Splitte Daten in Trainings und Test Daten\n", "train_test_data = train_test_split(data,\n", " continuous_target,\n", " test_size=0.2,\n", " random_state=random_state)\n", "training_data = train_test_data[0]\n", "testing_data = train_test_data[1]\n", "training_target = train_test_data[2]\n", "testing_target = train_test_data[3]\n", "\n", "print(\"Trainingsdaten\")\n", "print(\"Anzahl Patienten:\\t%d\"%training_data.shape[0])\n", "print(\"Anzahl Features:\\t%d\"%training_data.shape[1])\n", "print\n", "print(\"Testdaten\")\n", "print(\"Anzahl Patienten:\\t%d\"%testing_data.shape[0])\n", "print(\"Anzahl Features:\\t%d\"%testing_data.shape[1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.2 Trainiere Ridge Regression auf Trainingsdaten\n", "\n", "Als Erstes trainieren wir ein Ridge Regression Modell auf den Trainingsdaten mit einer 5-fachen Kreuzvalidierung und einer interenen Liniensuche, um das optimiale $\\alpha$ zu finden. Zusätzlich plotten wir den Trainingsfehler gegen den Subtestfehler, um den Effekt von unterschiedlichen $\\alpha$'s zu betrachten." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5-fache Kreuzvalidierung mit interner Liniensuche auf Trainingsdaten\n", "Durchschnitt(Mean Squared Error):\t\t587.09 (-+ 53.45)\n", "\n" ] }, { "data": { "image/png": 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kW/ukv7mR6C3mvUXBWAtfxyS/gu/G0Zy/4scOOQ//JHkSfjT+Bi0lnHPPmtm/8WNFHAMc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\nmRhKYRrOJbtGXpto9FlIEBhf4LYosNTPDr0421vqzSQ+zfzT3ALEgHt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7J8AHgAPV998B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b+F/vYbcH4KzMMSafAWf82R5fTes9Ko7+O13Pv/lltuecQ5d/352i2NlYzOdRroGQ09ar5HFIbs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\nnoi+HniBYPAkVItnX5f3kqR33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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Initialisiere Alphas für Ridge Regression\n", "alphas = sp.logspace(-2,8,11)\n", "param_grid = dict(alpha=alphas)\n", "\n", "#5-fach Kreuzvalidierung um den mittleren quadratischen Fehler zu berechnen\n", "outer_cv = KFold(n_splits=5,shuffle=True,random_state=random_state)\n", "\n", "#Liniensuche um das optimale Alpha zu finden\n", "line_search = GridSearchCV(Ridge(random_state=random_state,solver=\"cholesky\"),\n", " param_grid=param_grid,\n", " scoring=\"neg_mean_squared_error\")\n", "\n", "#Führe 5-fach Kreuzvalidierung mit interner Liniensuche aus und berechne mittleren quadratischen Fehler\n", "score = cross_val_score(line_search,X=training_data,y=training_target,cv=outer_cv,scoring=\"neg_mean_squared_error\")\n", "print(\"5-fache Kreuzvalidierung mit interner Liniensuche auf Trainingsdaten\")\n", "print(\"Durchschnitt(Mean Squared Error):\\t\\t%.2f (-+ %.2f)\"%(score.mean()*(-1),score.std()))\n", "print\n", "\n", "#Berechne optimales Alpha auf allen Trainings Daten\n", "line_search.fit(training_data,training_target)\n", "optimal_alpha = line_search.best_params_['alpha']\n", "\n", "#Visualisiere Trainings und Validierungs Fehler für unterschiedliche Alphas\n", "pl.figure(figsize=(18,7))\n", "pl.plot(alphas,line_search.cv_results_['mean_train_score']*(-1),label=\"Trainingsfehler\",color=\"#e67e22\")\n", "pl.fill_between(alphas,\n", " line_search.cv_results_['mean_train_score']*(-1)-line_search.cv_results_['std_train_score'],\n", " line_search.cv_results_['mean_train_score']*(-1)+line_search.cv_results_['std_train_score'],\n", " alpha=0.3,color=\"#e67e22\")\n", "pl.plot(alphas,line_search.cv_results_['mean_test_score']*(-1),label=\"Subtestfehler\",color=\"#2980b9\")\n", "pl.fill_between(alphas,\n", " line_search.cv_results_['mean_test_score']*(-1)-line_search.cv_results_['std_test_score'],\n", " line_search.cv_results_['mean_test_score']*(-1)+line_search.cv_results_['std_test_score'],\n", " alpha=0.3,color=\"#2980b9\")\n", "#pl.xlim(0,max(alphas))\n", "pl.xscale(\"log\")\n", "pl.ylabel(\"Mean Squared Error\")\n", "pl.xlabel(\"Alphas\")\n", "pl.legend(frameon=True)\n", "pl.grid(True)\n", "pl.axvline(x=optimal_alpha,color='r',linestyle=\"--\")\n", "pl.title(\"Training- vs. Subtest-Fehler (Optimales Alpha = %.1e)\"%optimal_alpha);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.3 Trainiere Ridge Regression mit optimalem $\\alpha$ und teste auf Testdaten\n", "Als Nächstes trainieren wir auf den gesamten Trainingsdaten ein Ridge Regression Modell mit dem optimalen $\\alpha$, welches wir in Abschnitt 1.2 mithilfe einer 5-fachen Kreuzvalidierung und einer interenen Liniensuche gefunden haben. Danach testen wir unser Modell auf den nicht verwendeten Testdaten, um zu sehen, wie gut unser Modell generalisiert." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ergebnisse mit optimalem Alpha auf Testdaten\n", "MSE (Test Daten, Alpha=Optimal):\t699.56 \n", "Optimales Alpha:\t\t\t100000.00\n", "\n" ] } ], "source": [ "#Trainiere Ridge Regression auf allen Trainings Daten mit optimalen Alpha\n", "model = Ridge(alpha=optimal_alpha,solver=\"cholesky\")\n", "model.fit(training_data,training_target)\n", "\n", "#Sage auf unbekannten Test Daten vorher\n", "predictions = model.predict(testing_data)\n", "print(\"Ergebnisse mit optimalem Alpha auf Testdaten\")\n", "print(\"MSE (Test Daten, Alpha=Optimal):\\t%.2f \"%(mean_squared_error(testing_target,predictions)))\n", "print(\"Optimales Alpha:\\t\\t\\t%.2f\"%optimal_alpha)\n", "print" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "
\n", "Eine 5-fache Kreuzvalidierung auf den Trainingsdaten führt zu einem Mean Squared Error von $MSE=587.09 \\pm 53.54$. Auf den Testdaten erhalten wir einen Fehler von $MSE=699.56$ ($\\sim 26.5$ Tage). Das bedeutet, dass das Ridge Regression Modell zu eher schlechten Vorhersagen führt (trotz Parameteroptimierung). \n", "Das kann z. B. daran liegen, dass die vorhandenen Features die genaue Zielvariable (Anzahl der Tage bis das Medikament wirkt) nur unzureichend beschreibt. \n", "
\n", "
\n", "
\n", "Wegen der eher entäuschenden Ergebnisse besprechen sich unsere Auftragsgeber nochmals. Nach längerer Beratung erklären uns diese, dass es ihnen eigentlich nicht auf die genaue Anzahl der Tage ankomme. Vielmehr würde ihnen ausreichen, wenn wir die Patienten vorhersagen könnten, welche eine längere bzw. eine kürzere Reaktionszeit haben. \n", "Laut den Erfahrungen unseres Auftraggebers ist ab circa 50 Tagen Anwendung mit erheblichen Nebenwirkungen zu rechen. Wir binarisieren unsere Daten, indem alle Patienten mit bis zu 50 Tagen in Klasse 0 und alle Patienten mit mehr als 50 Tagen in Klasse 1 aufgeteilt werden. \n", "Somit können wir statt einer Regression einen Klassifikationsalgorithmus verwenden, welcher versucht, die Patienten in diese zwei unterschiedlichen Klassen zu klassifizieren.\n", "
\n", "## 2. Vorhersage von Patienten mit Langsamer vs. Schneller Reaktionszeit mit einer Support-Vector-Machine\n", "\n", "### 2.1 Daten Vorverarbeitung" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Trainingsdaten\n", "Anzahl Patienten:\t\t320\n", "Anzahl Features:\t\t600\n", "Anzahl Patienten Klasse 0:\t160\n", "Anzahl Patienten Klasse 1:\t160\n", "\n", "Testdaten\n", "Anzahl Patienten:\t\t80\n", "Anzahl Features:\t\t600\n", "Anzahl Patienten Klasse 0:\t40\n", "Anzahl Patienten Klasse 1:\t40\n" ] } ], "source": [ "#Splitte Daten in Trainings und Test Daten unter Einhaltung der Klassen Ratio\n", "stratiefied_splitter = StratifiedShuffleSplit(n_splits=1,test_size=0.2,random_state=42)\n", "for train_index,test_index in stratiefied_splitter.split(data,binary_target):\n", " training_data = data[train_index,:]\n", " training_target = binary_target[train_index]\n", " testing_data = data[test_index,:]\n", " testing_target = binary_target[test_index]\n", "\n", "print(\"Trainingsdaten\")\n", "print(\"Anzahl Patienten:\\t\\t%d\"%training_data.shape[0])\n", "print(\"Anzahl Features:\\t\\t%d\"%training_data.shape[1])\n", "print(\"Anzahl Patienten Klasse 0:\\t%d\"%(training_target==0).sum())\n", "print(\"Anzahl Patienten Klasse 1:\\t%d\"%(training_target==1).sum())\n", "print\n", "print(\"Testdaten\")\n", "print(\"Anzahl Patienten:\\t\\t%d\"%testing_data.shape[0])\n", "print(\"Anzahl Features:\\t\\t%d\"%testing_data.shape[1])\n", "print(\"Anzahl Patienten Klasse 0:\\t%d\"%(testing_target==0).sum())\n", "print(\"Anzahl Patienten Klasse 1:\\t%d\"%(testing_target==1).sum())" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### 2.2 Klassifikation mit einer linearen SVM" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5-fache Kreuzvalidierung mit interner Liniensuche auf Trainingsdaten\n", "Durchschnitt(Accuracy):\t\t\t0.77 (-+ 0.06)\n", "\n", "Vorhersageergebnis mit optimalen C\n", "Accuracy (Test Daten, C=Optimal):\t0.82 \n", "Optimales C:\t\t\t\t1.00e-04\n", "\n" ] }, { "data": { "image/png": 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GRv0jbsbMfN4nNTW12jFVtNj46U9/ys9+9jPuvPNOOnfuTHp6Olu3buWWW26h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\nE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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Cs = sp.logspace(-7, 1, 9)\n", "param_grid = dict(C=Cs)\n", "\n", "grid = GridSearchCV(SVC(kernel=\"linear\",random_state=random_state),\n", " param_grid=param_grid,\n", " scoring=\"accuracy\",\n", " n_jobs=4)\n", "outer_cv = StratifiedKFold(n_splits=5,shuffle=True,random_state=random_state)\n", "\n", "#Perform 5 Fold cross-validation with internal line-search and report average Accuracy\n", "score = cross_val_score(grid,X=training_data,y=training_target,cv=outer_cv,scoring=\"accuracy\")\n", "print(\"5-fache Kreuzvalidierung mit interner Liniensuche auf Trainingsdaten\")\n", "print(\"Durchschnitt(Accuracy):\\t\\t\\t%.2f (-+ %.2f)\"%(score.mean(),score.std()))\n", "print\n", "grid.fit(training_data,training_target)\n", "optimal_C = grid.best_params_['C']\n", "\n", "pl.figure(figsize=(18,7))\n", "pl.plot(Cs,grid.cv_results_['mean_train_score'],label=\"Trainingsfehler\",color=\"#e67e22\")\n", "pl.fill_between(Cs,\n", " grid.cv_results_['mean_train_score']-grid.cv_results_['std_train_score'],\n", " grid.cv_results_['mean_train_score']+grid.cv_results_['std_train_score'],\n", " alpha=0.3,color=\"#e67e22\")\n", "pl.plot(Cs,grid.cv_results_['mean_test_score'],label=\"Subtestfehler\",color=\"#2980b9\")\n", "pl.fill_between(Cs,\n", " grid.cv_results_['mean_test_score']-grid.cv_results_['std_test_score'],\n", " grid.cv_results_['mean_test_score']+grid.cv_results_['std_test_score'],\n", " alpha=0.3,color=\"#2980b9\")\n", "pl.ylabel(\"Accuracy\")\n", "pl.xlabel(\"Cs\")\n", "pl.xscale(\"log\")\n", "pl.legend(frameon=True)\n", "pl.grid(True)\n", "pl.axvline(x=optimal_C,color='r',linestyle=\"--\")\n", "pl.title(\"Training- vs. Subtest-Fehler (Optimales C = %.2e)\"%optimal_C);\n", "\n", "model = SVC(C=optimal_C,random_state=random_state,kernel=\"linear\")\n", "model.fit(training_data,training_target)\n", "predictions = model.predict(testing_data)\n", "print(\"Vorhersageergebnis mit optimalen C\")\n", "print(\"Accuracy (Test Daten, C=Optimal):\\t%.2f \"%(accuracy_score(testing_target,predictions)))\n", "print(\"Optimales C:\\t\\t\\t\\t%.2e\"%optimal_C)\n", "print" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.3 Klassifikation mit einer SVM mit RBF Kern\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5-fache Kreuzvalidierung mit interner Liniensuche auf Trainingsdaten\n", "Durchschnitt(Accuracy):\t\t\t0.77 (-+ 0.06)\n", "\n", "Vorhersageergebnis mit optimalen C und Gamma\n", "Accuracy (Test Daten, C=Optimal):\t0.93 \n", "Optimales C:\t\t\t\t1.00e+01\n", "Optimales Gamma:\t\t\t1.00e-05\n", "\n" ] } ], "source": [ "Cs = sp.logspace(-4, 4, 9)\n", "gammas = sp.logspace(-7, 1, 9)\n", "param_grid = dict(C=Cs,gamma=gammas)\n", "\n", "grid = GridSearchCV(SVC(kernel=\"rbf\",random_state=42),\n", " param_grid=param_grid,\n", " scoring=\"accuracy\",\n", " n_jobs=4)\n", "\n", "outer_cv = StratifiedKFold(n_splits=5,shuffle=True,random_state=random_state)\n", "\n", "#Perform 5 Fold cross-validation with internal line-search and report average Accuracyscore = cross_val_score(grid,X=training_data,y=training_target,cv=outer_cv,scoring=\"accuracy\")\n", "print(\"5-fache Kreuzvalidierung mit interner Liniensuche auf Trainingsdaten\")\n", "print(\"Durchschnitt(Accuracy):\\t\\t\\t%.2f (-+ %.2f)\"%(score.mean(),score.std()))\n", "print\n", "\n", "grid.fit(training_data,training_target)\n", "optimal_C = grid.best_params_['C']\n", "optimal_gamma = grid.best_params_['gamma']\n", "\n", "model = SVC(C=optimal_C,gamma=optimal_gamma,random_state=42,kernel=\"rbf\")\n", "model.fit(training_data,training_target)\n", "predictions = model.predict(testing_data)\n", "print(\"Vorhersageergebnis mit optimalen C und Gamma\")\n", "print(\"Accuracy (Test Daten, C=Optimal):\\t%.2f \"%(accuracy_score(testing_target,predictions)))\n", "print(\"Optimales C:\\t\\t\\t\\t%.2e\"%optimal_C)\n", "print(\"Optimales Gamma:\\t\\t\\t%.2e\"%optimal_gamma)\n", "print" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "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": 2 }