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@tungwaiyip
Created May 11, 2015 22:00
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{
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"from scipy import stats\n",
"from sklearn.svm import SVC"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"colon_cancer = np.loadtxt(\"colon_cancer.txt\")\n",
"colon_healthy = np.loadtxt(\"colon_healthy.txt\")\n",
"colon_test = np.loadtxt(\"colon_test.txt\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# stack cancer and healthy into data\n",
"N = colon_cancer.shape[0] + colon_healthy.shape[0]\n",
"\n",
"y = np.zeros(N, dtype=np.int32)\n",
"y[0:colon_cancer.shape[0]] = 1\n",
"\n",
"data = vstack([colon_cancer, colon_healthy])\n",
"\n",
"# split into training set and test set\n",
"np.random.seed(5643134)\n",
"training_flag = np.random.rand(N) < 0.8\n",
"test_flag = training_flag == False\n",
"\n",
"data_training = data[training_flag]\n",
"data_test = data[test_flag]\n",
"y_training = y[training_flag]\n",
"y_test = y[test_flag]\n",
"\n",
"print(\"Data=%s Training=%s Cancer Rate=%0.2f Test=%s Cancer Rate=%0.2f\" % (\n",
" N, \n",
" sum(training_flag), \n",
" 1. * sum(y_training) / sum(training_flag),\n",
" sum(test_flag),\n",
" 1. * sum(y_test) / sum(test_flag),\n",
"))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Data=61 Training=47 Cancer Rate=0.60 Test=14 Cancer Rate=0.86\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# TODO: try non-linear kernel\n",
"clf = SVC(kernel='linear', probability=True)\n",
"clf.fit(data_training, y_training)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,\n",
" kernel='linear', max_iter=-1, probability=True, random_state=None,\n",
" shrinking=True, tol=0.001, verbose=False)"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"prob_t0 = clf.predict_proba(data_training[y_training == 0])\n",
"prob_t1 = clf.predict_proba(data_training[y_training == 1])\n",
"prob_0 = clf.predict_proba(data_test[y_test == 0])\n",
"prob_1 = clf.predict_proba(data_test[y_test == 1])\n",
"prob_sample = clf.predict_proba(colon_test)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"density_t0 = stats.gaussian_kde(prob_t0[:,1])\n",
"density_t1 = stats.gaussian_kde(prob_t1[:,1])\n",
"density_0 = stats.gaussian_kde(prob_0[:,1])\n",
"density_1 = stats.gaussian_kde(prob_1[:,1])\n",
"\n",
"xs = np.linspace(0,1,100)\n",
"xlim([0.,1.3])\n",
"ylim([0,10])\n",
"plot(xs, density_t0(xs), color=\"g\", ls=\"dotted\", label=\"healthy training\")\n",
"plot(xs, density_t1(xs), color=\"r\", ls=\"dotted\", label=\"cancer training\")\n",
"plot(xs, density_0(xs), color=\"g\", label=\"healthy test\")\n",
"plot(xs, density_1(xs), color=\"r\", label=\"cancer test\")\n",
"axvline(x=prob_sample[0,1], color=\"b\", label=\"sample\")\n",
"xlabel(\"Cancer group probablity\")\n",
"ylabel(\"Density\")\n",
"title(\"Cancer prediction by SVM\")\n",
"legend(loc=\"upper right\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": [
"<matplotlib.legend.Legend at 0xd106930>"
]
},
{
"metadata": {},
"output_type": "display_data",
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lJSURh8MhkUhUi09UefKuk6quH7ZMkYH55+U/aGzVGFb1rRQq/1fyXwD6qjco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\nJRKJpAnwf6qWaHZ4yO7YAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x76c05b0>"
]
}
],
"prompt_number": 7
}
],
"metadata": {}
}
]
}
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