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

\n", " \n", "
\n", " In this toy example we will compare two machine learning models: Ridge Regression and C-SVM. The data is generated in silico and is only used to illustrate how to use Ridge Regression and C-SVM.\n", "
\n", "\n", "## Problem Description of the Toy Example\n", "

\n", " \n", "
\n", "A new cancer drug was developed for therapy. During the clinical trail the researchers releaized that the drug had a faster response for a certain subgroup of the patients, while it was less responsive in the others. In addition, the researchers recognized that the drug leads to severe side-effects the longer the patient is treated with the drug. The goal should be to reduce the side effects by treating only those patients that are predicted to have a fast response when taking the drug.\n", "
\n", "
\n", "
\n", "The researches believe that different genetic mutations in the genomes of the individual patients might play a role for the differences in response times.\n", "
\n", "
\n", "
\n", " The researches contacted the machine learning lab to build a predictive model. The model should predict the individual response time of the drug based on the individual genetic backgrounds of a patient.\n", "
\n", "
\n", "
\n", "For this purpose, we get a dataset of 400 patients. For each patient a panel of 600 genetic mutations was measured. In addition, the researchers measured how many days it took until the drug showed a positive response.\n", "
\n", "\n", "\n", "## 1. Using Ridge Regression to predict the response time\n", "
\n", " To predict the response time of the drug for new patients, we will train a Ridge Regression model. The target variable for this task is the response time in days. The features are the 600 genetic mutations measured for each of the 400 patients. To avoid overfitting we will use a nested-crossvalidation to determine the optimal hyperparamter.\n", "
\n", "### 1.1 Data Preprocessing" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Orginal Data\n", "Number Patients:\t400\n", "Number Features:\t600\n", "\n", "Training Data\n", "Number Patients:\t320\n", "Number Features:\t600\n", "\n", "Testing Data\n", "Number Patients:\t80\n", "Number 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", "from sklearn.metrics import roc_curve, auc\n", "\n", "def visualized_variance_bias_tradeoff(hyperp, line_search, optimal_hyperp,classification=False):\n", " pl.figure(figsize=(18,7))\n", " if classification:\n", " factor=1\n", " else:\n", " factor=-1\n", " pl.plot(hyperp,line_search.cv_results_['mean_train_score']*factor,label=\"Training Error\",color=\"#e67e22\")\n", " pl.fill_between(hyperp,\n", " line_search.cv_results_['mean_train_score']*factor-line_search.cv_results_['std_train_score'],\n", " line_search.cv_results_['mean_train_score']*factor+line_search.cv_results_['std_train_score'],\n", " alpha=0.3,color=\"#e67e22\")\n", " pl.plot(hyperp,line_search.cv_results_['mean_test_score']*factor,label=\"Validation Error\",color=\"#2980b9\")\n", " pl.fill_between(hyperp,\n", " line_search.cv_results_['mean_test_score']*factor-line_search.cv_results_['std_test_score'],\n", " line_search.cv_results_['mean_test_score']*factor+line_search.cv_results_['std_test_score'],\n", " alpha=0.3,color=\"#2980b9\")\n", " pl.xscale(\"log\")\n", " if classification:\n", " pl.ylabel(\"Accuracy\")\n", " else:\n", " pl.ylabel(\"Mean Squared Error\")\n", " pl.xlabel(\"Hyperparameter\")\n", " pl.legend(frameon=True)\n", " pl.grid(True)\n", " pl.axvline(x=optimal_hyperp,color='r',linestyle=\"--\")\n", " pl.title(\"Training- vs. Validation-Error (Optimal Hyperparameter = %.1e)\"%optimal_hyperp);\n", "\n", "random_state = 42\n", "\n", "#Load Data\n", "data = sp.loadtxt(\"data/X.txt\")\n", "binary_target = sp.loadtxt(\"data/y_binary.txt\")\n", "continuous_target = sp.loadtxt(\"data/y.txt\")\n", "\n", "#Summary of the Data\n", "print(\"Orginal Data\")\n", "print(\"Number Patients:\\t%d\"%data.shape[0])\n", "print(\"Number Features:\\t%d\"%data.shape[1])\n", "print()\n", "\n", "#Split Data into Training and Testing data\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(\"Training Data\")\n", "print(\"Number Patients:\\t%d\"%training_data.shape[0])\n", "print(\"Number Features:\\t%d\"%training_data.shape[1])\n", "print()\n", "print(\"Testing Data\")\n", "print(\"Number Patients:\\t%d\"%testing_data.shape[0])\n", "print(\"Number Features:\\t%d\"%testing_data.shape[1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.2 Train Ridge Regression on training data\n", "\n", "The first step is to train the ridge regression model on the training data with a **5-fold cross-validation** with an **internal line-search** to find the **optimal hyperparameter $\\alpha$**. We will plot the **training errors** against the **validation errors**, to illustrate the effect of different $\\alpha$ values." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5-fold nested cross-validation\n", "Mean-Squared-Error:\t\t587.09 (-+ 53.45)\n", "\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Initialize different alpha values for the Ridge Regression model\n", "alphas = sp.logspace(-2,8,11)\n", "param_grid = dict(alpha=alphas)\n", "\n", "#5-fold cross-validation (outer-loop)\n", "outer_cv = KFold(n_splits=5,shuffle=True,random_state=random_state)\n", "\n", "#Line-search to find the optimal alpha value (internal-loop)\n", "#Model performance is measured with the negative mean squared error\n", "line_search = GridSearchCV(Ridge(random_state=random_state,solver=\"cholesky\"),\n", " param_grid=param_grid,\n", " scoring=\"neg_mean_squared_error\",\n", " return_train_score=True)\n", "\n", "#Execute nested cross-validation and compute mean squared error\n", "score = cross_val_score(line_search,X=training_data,y=training_target,cv=outer_cv,scoring=\"neg_mean_squared_error\")\n", "\n", "print(\"5-fold nested cross-validation\")\n", "print(\"Mean-Squared-Error:\\t\\t%.2f (-+ %.2f)\"%(score.mean()*(-1),score.std()))\n", "print()\n", "\n", "#Estimate optimal alpha on the full training data\n", "line_search.fit(training_data,training_target)\n", "optimal_alpha = line_search.best_params_['alpha']\n", "\n", "#Visualize training and validation error for different alphas\n", "visualized_variance_bias_tradeoff(alphas, line_search, optimal_alpha)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.3 Train Ridge Regression with optimal $\\alpha$ and evaluate model in test data\n", "Next we retrain the ridge regresssion model with the optimal $\\alpha$ (from the last section). After re-training we will test the model on the not used test data to evaluate the model performance on unseen data." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Prediction results on test data\n", "MSE (test data, alpha=optimal):\t699.56 \n", "Optimal Alpha:\t\t\t100000.00\n", "\n" ] } ], "source": [ "#Train Ridge Regression on the full training data with optimal alpha\n", "model = Ridge(alpha=optimal_alpha,solver=\"cholesky\")\n", "model.fit(training_data,training_target)\n", "\n", "#Use trained model the predict new instances in test data\n", "predictions = model.predict(testing_data)\n", "print(\"Prediction results on test data\")\n", "print(\"MSE (test data, alpha=optimal):\\t%.2f \"%(mean_squared_error(testing_target,predictions)))\n", "print(\"Optimal Alpha:\\t\\t\\t%.2f\"%optimal_alpha)\n", "print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "
\n", " Using 5-fold cross-validation on the training data leads to a mean squared error (MSE) of $MSE=587.09 \\pm 53.54$. On the test data we get an error of $MSE=699.56$ ($\\sim 26.5$ days). That indicates that the ridge regression model performs rather mediocre (even with hyperparameter optimization).\n", " One reason might be that the target variable (number of days until the drug shows a positive response) is insufficently described by the given features (genetic mutations).\n", "
\n", "\n", "\n", "## 2. Prediction of patients with slow and fast response times using a Support-Vector-Machine \n", "\n", "
\n", " Due to the rather bad results with the ridge regession model the machine learning lab returned to the researchers to discuss potential issues. The researches than mentioned that it might not be necessarily important to predict the exact number of days. It might be even better to only predict if a patient reacts fast or slowly on the drug. Based on some prior experiments the researchers observed, that most of the patients showed severe side-effects after 50 days of treatment. Thus we can binarise the data, such that all patients below 50 days are put into class 0 and all others into class 1. This leads to a classical classification problem for which a support vector machine could be used. \n", "
\n", "\n", "### 2.1 Data Preprocessing" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training Data\n", "Number Patients:\t\t320\n", "Number Features:\t\t600\n", "Number Patients Class 0:\t160\n", "Number Patients Class 1:\t160\n", "\n", "Testing Data\n", "Number Patients:\t\t80\n", "Number Features:\t\t600\n", "Number Patients Class 0:\t40\n", "Number Patients Class 1:\t40\n" ] } ], "source": [ "#Split data into training and testing splits, stratified by class-ratios\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(\"Training Data\")\n", "print(\"Number Patients:\\t\\t%d\"%training_data.shape[0])\n", "print(\"Number Features:\\t\\t%d\"%training_data.shape[1])\n", "print(\"Number Patients Class 0:\\t%d\"%(training_target==0).sum())\n", "print(\"Number Patients Class 1:\\t%d\"%(training_target==1).sum())\n", "print()\n", "print(\"Testing Data\")\n", "print(\"Number Patients:\\t\\t%d\"%testing_data.shape[0])\n", "print(\"Number Features:\\t\\t%d\"%testing_data.shape[1])\n", "print(\"Number Patients Class 0:\\t%d\"%(testing_target==0).sum())\n", "print(\"Number Patients Class 1:\\t%d\"%(testing_target==1).sum())" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### 2.2 Classification with a linear SVM" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5-fold nested cross-validation on training data\n", "Average(Accuracy):\t\t\t0.80 (-+ 0.02)\n", "\n", "Prediction with optimal C\n", "Accuracy (Test data, C=Optimal):\t0.82 \n", "Optimal C:\t\t\t\t1.00e-04\n", "\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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\n", 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" ] }, "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", " return_train_score=True)\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", "\n", "print(\"5-fold nested cross-validation on training data\")\n", "print(\"Average(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", "#Plot variance bias tradeoff\n", "visualized_variance_bias_tradeoff(Cs, grid, optimal_C,classification=True)\n", "\n", "#retrain model with optimal C and evaluate on test data\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(\"Prediction with optimal C\")\n", "print(\"Accuracy (Test data, C=Optimal):\\t%.2f \"%(accuracy_score(testing_target,predictions)))\n", "print(\"Optimal C:\\t\\t\\t\\t%.2e\"%optimal_C)\n", "print()\n", "\n", "#Compute ROC FPR, TPR and AUC\n", "fpr, tpr, _ = roc_curve(testing_target, model.decision_function(testing_data))\n", "roc_auc = auc(fpr, tpr)\n", "\n", "#Plot ROC Curve\n", "pl.figure(figsize=(8,8))\n", "pl.plot(fpr, tpr, color='darkorange',\n", " lw=3, label='ROC curve (AUC = %0.2f)' % roc_auc)\n", "pl.plot([0, 1], [0, 1], color='navy', lw=3, linestyle='--')\n", "pl.xlim([-0.01, 1.0])\n", "pl.ylim([0.0, 1.05])\n", "pl.xlabel('False Positive Rate (1-Specificity)',fontsize=18)\n", "pl.ylabel('True Positive Rate (Sensitivity)',fontsize=18)\n", "pl.title('Receiver Operating Characteristic (ROC) Curve',fontsize=18)\n", "pl.legend(loc=\"lower right\",fontsize=18)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.3 Classification with SVM and RBF kernel\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5-fold nested cross-validation on training data\n", "Average(Accuracy):\t\t\t0.86 (-+ 0.02)\n", "\n", "Prediction with optimal C and Gamma\n", "Accuracy (Test Data, C=Optimal):\t0.93 \n", "Optimal C:\t\t\t\t1.00e+01\n", "Optimal Gamma:\t\t\t\t1.00e-05\n", "\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "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", " return_train_score=True)\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 Accuracy\n", "score = cross_val_score(grid,X=training_data,y=training_target,cv=outer_cv,scoring=\"accuracy\")\n", "\n", "print(\"5-fold nested cross-validation on training data\")\n", "print(\"Average(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", "#Retrain and test\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(\"Prediction with optimal C and Gamma\")\n", "print(\"Accuracy (Test Data, C=Optimal):\\t%.2f \"%(accuracy_score(testing_target,predictions)))\n", "print(\"Optimal C:\\t\\t\\t\\t%.2e\"%optimal_C)\n", "print(\"Optimal Gamma:\\t\\t\\t\\t%.2e\"%optimal_gamma)\n", "print()\n", "\n", "#Compute ROC FPR, TPR and AUC\n", "fpr, tpr, _ = roc_curve(testing_target, model.decision_function(testing_data))\n", "roc_auc = auc(fpr, tpr)\n", "\n", "#Plot ROC Curve\n", "pl.figure(figsize=(8,8))\n", "pl.plot(fpr, tpr, color='darkorange',\n", " lw=3, label='ROC curve (AUC = %0.2f)' % roc_auc)\n", "pl.plot([0, 1], [0, 1], color='navy', lw=3, linestyle='--')\n", "pl.xlim([-0.01, 1.0])\n", "pl.ylim([0.0, 1.05])\n", "pl.xlabel('False Positive Rate (1-Specificity)',fontsize=18)\n", "pl.ylabel('True Positive Rate (Sensitivity)',fontsize=18)\n", "pl.title('Receiver Operating Characteristic (ROC) Curve',fontsize=18)\n", "pl.legend(loc=\"lower right\",fontsize=18)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }