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@NickRSearcy
Last active December 16, 2015 13:18
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Final Data Mining Project
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{
"metadata": {
"name": "Digits"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sklearn import *"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"digits = loadtxt(open('Data/Digits/train.csv',\"rb\"), delimiter = \",\", skiprows=1,dtype=float64)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"n_points = digits.shape[0]\n",
"test_percent = 50\n",
"random_order = random.permutation(n_points)\n",
"train_index = int((1 - test_percent/float(100))*n_points)\n",
"\n",
"labels = digits[:,0]\n",
"#data = preprocessing.scale(digits[:,1:]) #preprocessing offered no improvement\n",
"data = digits[:,1:]\n",
"\n",
"train_labels = labels[random_order[:train_index]]\n",
"test_lables = labels[random_order[train_index:]]\n",
"train_data = data[random_order[:train_index],:]\n",
"test_data = data[random_order[train_index:],:]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"###############################################################################\n",
"# Visualize the results on PCA-reduced data\n",
"\n",
"n_display = 40\n",
"display_step = n_points/n_display\n",
"display_data = data[0:-1:display_step,:]\n",
"display_labels = labels[0:-1:display_step]\n",
"\n",
"reduced_data = decomposition.PCA(n_components=2).fit_transform(display_data)\n",
"svc_r = svm.SVC(kernel='poly',degree=3)\n",
"svc_r.fit(reduced_data,display_labels)\n",
"\n",
"# Step size of the mesh. Decrease to increase the quality of the VQ.\n",
"h = 400 # point in the mesh [x_min, m_max]x[y_min, y_max].\n",
"\n",
"# Plot the decision boundary. For that, we will asign a color to each\n",
"x_min, x_max = reduced_data[:, 0].min() + 1, reduced_data[:, 0].max() - 1\n",
"y_min, y_max = reduced_data[:, 1].min() + 1, reduced_data[:, 1].max() - 1\n",
"xx, yy = np.meshgrid(linspace(x_min, x_max, h), linspace(y_min, y_max, h))\n",
"\n",
"# Obtain labels for each point in mesh. Use last trained model.\n",
"Z = svc_r.predict(c_[xx.ravel(), yy.ravel()])\n",
"\n",
"# Put the result into a color plot\n",
"Z = Z.reshape(xx.shape)\n",
"figsize(10,10)\n",
"figure()\n",
"contourf(xx, yy, Z, cmap=cm.Paired)\n",
"axis('off')\n",
"scatter(reduced_data[:, 0], reduced_data[:, 1], c=display_labels, cmap=cm.Paired)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 4,
"text": [
"<matplotlib.collections.PathCollection at 0x107a6aa50>"
]
},
{
"output_type": "display_data",
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LuCk4YBgdMHgiyItXgxK+JKmmpl5du+YqNzdbkpSTk6Xu3fNUW8dtclwzv7SAbhbgMAIY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\njAAGAADgMwIYAACAz/4/ppGjXUnKhwAAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x107c32e90>"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"knn = neighbors.KNeighborsClassifier()\n",
"knn.fit(train_data, train_labels)\n",
"predicted_labels = knn.predict(test_data)\n",
"print(metrics.classification_report(test_lables,predicted_labels))\n",
"print(metrics.confusion_matrix(test_lables,predicted_labels))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.97 0.99 0.98 2027\n",
" 1 0.94 0.99 0.97 2383\n",
" 2 0.98 0.95 0.96 2128\n",
" 3 0.94 0.96 0.95 2111\n",
" 4 0.98 0.95 0.97 2034\n",
" 5 0.95 0.95 0.95 1938\n",
" 6 0.98 0.98 0.98 2101\n",
" 7 0.95 0.97 0.96 2134\n",
" 8 0.98 0.90 0.94 2006\n",
" 9 0.94 0.94 0.94 2138\n",
"\n",
"avg / total 0.96 0.96 0.96 21000\n",
"\n",
"[[2012 2 2 0 0 3 8 0 0 0]\n",
" [ 0 2371 5 2 0 0 2 3 0 0]\n",
" [ 22 29 2012 12 0 2 1 37 9 4]\n",
" [ 3 5 13 2026 0 25 3 14 12 10]\n",
" [ 3 24 0 0 1941 0 4 4 1 57]\n",
" [ 5 5 3 37 2 1842 20 3 5 16]\n",
" [ 15 6 2 0 2 13 2062 0 1 0]\n",
" [ 1 30 6 0 5 0 0 2073 0 19]\n",
" [ 7 36 14 51 8 51 12 7 1802 18]\n",
" [ 9 7 4 23 25 4 1 44 5 2016]]\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"nb = naive_bayes.GaussianNB()\n",
"nb.fit(test_data,test_lables)\n",
"predicted_labels = nb.predict(test_data)\n",
"print(metrics.classification_report(test_lables,predicted_labels))\n",
"print(metrics.confusion_matrix(test_lables,predicted_labels))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.75 0.79 0.77 2027\n",
" 1 0.70 0.96 0.81 2383\n",
" 2 0.90 0.19 0.31 2128\n",
" 3 0.65 0.29 0.40 2111\n",
" 4 0.78 0.09 0.16 2034\n",
" 5 0.57 0.02 0.04 1938\n",
" 6 0.57 0.94 0.71 2101\n",
" 7 0.93 0.26 0.41 2134\n",
" 8 0.25 0.61 0.35 2006\n",
" 9 0.40 0.92 0.56 2138\n",
"\n",
"avg / total 0.65 0.52 0.46 21000\n",
"\n",
"[[1611 4 6 4 4 2 170 1 188 37]\n",
" [ 1 2299 2 6 0 1 28 1 37 8]\n",
" [ 198 115 397 226 11 10 605 6 532 28]\n",
" [ 79 137 5 606 3 2 174 10 943 152]\n",
" [ 53 36 15 13 186 5 265 5 499 957]\n",
" [ 159 80 7 34 5 42 167 2 1271 171]\n",
" [ 11 64 6 2 2 3 1978 0 33 2]\n",
" [ 6 43 0 19 12 1 11 553 144 1345]\n",
" [ 25 442 0 12 8 6 50 0 1225 238]\n",
" [ 10 57 5 5 9 2 5 17 52 1976]]\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"svc = svm.SVC(kernel='poly',degree=3)\n",
"svc.fit(test_data,test_lables)\n",
"predicted_labels = svc.predict(test_data)\n",
"print(metrics.classification_report(test_lables,predicted_labels))\n",
"print(metrics.confusion_matrix(test_lables,predicted_labels))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 1.00 1.00 1.00 2027\n",
" 1 1.00 1.00 1.00 2383\n",
" 2 1.00 1.00 1.00 2128\n",
" 3 1.00 1.00 1.00 2111\n",
" 4 1.00 1.00 1.00 2034\n",
" 5 1.00 1.00 1.00 1938\n",
" 6 1.00 1.00 1.00 2101\n",
" 7 1.00 1.00 1.00 2134\n",
" 8 1.00 1.00 1.00 2006\n",
" 9 1.00 1.00 1.00 2138\n",
"\n",
"avg / total 1.00 1.00 1.00 21000\n",
"\n",
"[[2027 0 0 0 0 0 0 0 0 0]\n",
" [ 0 2383 0 0 0 0 0 0 0 0]\n",
" [ 0 0 2128 0 0 0 0 0 0 0]\n",
" [ 0 0 0 2111 0 0 0 0 0 0]\n",
" [ 0 0 0 0 2034 0 0 0 0 0]\n",
" [ 0 0 0 0 0 1938 0 0 0 0]\n",
" [ 0 0 0 0 0 0 2101 0 0 0]\n",
" [ 0 0 0 0 0 0 0 2134 0 0]\n",
" [ 0 0 0 0 0 0 0 0 2006 0]\n",
" [ 0 0 0 0 0 0 0 0 0 2138]]\n"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"n_display = 9\n",
"n_side = int(sqrt(n_display))\n",
"\n",
"for i in range(n_display):\n",
" subplot(n_side, n_side, i + 1)\n",
" image = digits[i,1:]\n",
" image = reshape(image,(28,28))\n",
" imshow(image, cmap=cm.gray)\n",
" title('Label: ' + str(labels[i]))\n",
" axis('off')\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
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ODnxFDtLmMbTLbtbzzxt4zKxbvF9bTOH7dswSXhOy13AggGXpuBYl/Xd0Nl40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\nPbiDAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x107c12550>"
]
}
],
"prompt_number": "*"
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"LR = linear_model.LogisticRegression()\n",
"LR.fit(test_data,test_lables)\n",
"predicted_labels = LR.predict(test_data)\n",
"print(metrics.classification_report(test_lables,predicted_labels))\n",
"print(metrics.confusion_matrix(test_lables,predicted_labels))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": "*"
}
],
"metadata": {}
}
]
}
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