{ "cells": [ { "cell_type": "code", "execution_count": 0, "metadata": { "collapsed": false }, "outputs": [ ], "source": [ ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ ], "source": [ "import numpy as np\n", "import pandas as pd\n", "from sklearn import neighbors, metrics, decomposition\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['class', 'Alcohol', 'Malic acid', 'Ash', 'Alcalinity of ash', 'Magnesium', 'Total phenols', 'Flavanoids', 'Nonflavanoid phenols', 'Proanthocyanins', 'Color intensity', 'Hue', 'OD280/OD315 of diluted wines', '']\n" ] } ], "source": [ "attr_names=\"\"\"class\n", "Alcohol\n", "Malic acid\n", "Ash\n", "Alcalinity of ash\n", "Magnesium\n", "Total phenols\n", "Flavanoids\n", "Nonflavanoid phenols\n", "Proanthocyanins\n", "Color intensity\n", "Hue\n", "OD280/OD315 of diluted wines\n", "\"\"\"\n", "attr_names = attr_names.split(\"\\n\")\n", "print(attr_names)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ ], "source": [ "df = pd.read_csv('data/wine.data',sep =',',names=attr_names )" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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classAlcoholMalic acidAshAlcalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 of diluted wines
0114.231.712.4315.61272.803.060.282.295.6400001.043.921065
1113.201.782.1411.21002.652.760.261.284.3800001.053.401050
2113.162.362.6718.61012.803.240.302.815.6800001.033.171185
3114.371.952.5016.81133.853.490.242.187.8000000.863.451480
4113.242.592.8721.01182.802.690.391.824.3200001.042.93735
5114.201.762.4515.21123.273.390.341.976.7500001.052.851450
6114.391.872.4514.6962.502.520.301.985.2500001.023.581290
7114.062.152.6117.61212.602.510.311.255.0500001.063.581295
8114.831.642.1714.0972.802.980.291.985.2000001.082.851045
9113.861.352.2716.0982.983.150.221.857.2200001.013.551045
10114.102.162.3018.01052.953.320.222.385.7500001.253.171510
11114.121.482.3216.8952.202.430.261.575.0000001.172.821280
12113.751.732.4116.0892.602.760.291.815.6000001.152.901320
13114.751.732.3911.4913.103.690.432.815.4000001.252.731150
14114.381.872.3812.01023.303.640.292.967.5000001.203.001547
15113.631.812.7017.21122.852.910.301.467.3000001.282.881310
16114.301.922.7220.01202.803.140.331.976.2000001.072.651280
17113.831.572.6220.01152.953.400.401.726.6000001.132.571130
18114.191.592.4816.51083.303.930.321.868.7000001.232.821680
19113.643.102.5615.21162.703.030.171.665.1000000.963.36845
20114.061.632.2816.01263.003.170.242.105.6500001.093.71780
21112.933.802.6518.61022.412.410.251.984.5000001.033.52770
22113.711.862.3616.61012.612.880.271.693.8000001.114.001035
23112.851.602.5217.8952.482.370.261.463.9300001.093.631015
24113.501.812.6120.0962.532.610.281.663.5200001.123.82845
25113.052.053.2225.01242.632.680.471.923.5800001.133.20830
26113.391.772.6216.1932.852.940.341.454.8000000.923.221195
27113.301.722.1417.0942.402.190.271.353.9500001.022.771285
28113.871.902.8019.41072.952.970.371.764.5000001.253.40915
29114.021.682.2116.0962.652.330.261.984.7000001.043.591035
.............................................
148313.323.242.3821.5921.930.760.451.258.4200000.551.62650
149313.083.902.3621.51131.411.390.341.149.4000000.571.33550
150313.503.122.6224.01231.401.570.221.258.6000000.591.30500
151312.792.672.4822.01121.481.360.241.2610.8000000.481.47480
152313.111.902.7525.51162.201.280.261.567.1000000.611.33425
153313.233.302.2818.5981.800.830.611.8710.5200000.561.51675
154312.581.292.1020.01031.480.580.531.407.6000000.581.55640
155313.175.192.3222.0931.740.630.611.557.9000000.601.48725
156313.844.122.3819.5891.800.830.481.569.0100000.571.64480
157312.453.032.6427.0971.900.580.631.147.5000000.671.73880
158314.341.682.7025.0982.801.310.532.7013.0000000.571.96660
159313.481.672.6422.5892.601.100.522.2911.7500000.571.78620
160312.363.832.3821.0882.300.920.501.047.6500000.561.58520
161313.693.262.5420.01071.830.560.500.805.8800000.961.82680
162312.853.272.5822.01061.650.600.600.965.5800000.872.11570
163312.963.452.3518.51061.390.700.400.945.2800000.681.75675
164313.782.762.3022.0901.350.680.411.039.5800000.701.68615
165313.734.362.2622.5881.280.470.521.156.6200000.781.75520
166313.453.702.6023.01111.700.920.431.4610.6800000.851.56695
167312.823.372.3019.5881.480.660.400.9710.2600000.721.75685
168313.582.582.6924.51051.550.840.391.548.6600000.741.80750
169313.404.602.8625.01121.980.960.271.118.5000000.671.92630
170312.203.032.3219.0961.250.490.400.735.5000000.661.83510
171312.772.392.2819.5861.390.510.480.649.8999990.571.63470
172314.162.512.4820.0911.680.700.441.249.7000000.621.71660
173313.715.652.4520.5951.680.610.521.067.7000000.641.74740
174313.403.912.4823.01021.800.750.431.417.3000000.701.56750
175313.274.282.2620.01201.590.690.431.3510.2000000.591.56835
176313.172.592.3720.01201.650.680.531.469.3000000.601.62840
177314.134.102.7424.5962.050.760.561.359.2000000.611.60560
\n", "

178 rows × 14 columns

\n", "
" ] }, "execution_count": 4, "metadata": { }, "output_type": "execute_result" } ], "source": [ "\n", "df" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ ], "source": [ "X1=df[attr_names[1]].values\n", "X2=df[attr_names[2]].values\n", "X3=df[attr_names[3]].values\n", "X4=df[attr_names[4]].values\n", "X5=df[attr_names[5]].values\n", "X6=df[attr_names[6]].values\n", "X7=df[attr_names[7]].values\n", "X8=df[attr_names[8]].values\n", "X9=df[attr_names[9]].values\n", "X10=df[attr_names[10]].values\n", "X11=df[attr_names[11]].values\n", "X12=df[attr_names[12]].values" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ ], "source": [ "Y= df[attr_names[0]].values" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 14.23 1.71 2.43 ..., 5.64 1.04 3.92]\n", " [ 13.2 1.78 2.14 ..., 4.38 1.05 3.4 ]\n", " [ 13.16 2.36 2.67 ..., 5.68 1.03 3.17]\n", " ..., \n", " [ 13.27 4.28 2.26 ..., 10.2 0.59 1.56]\n", " [ 13.17 2.59 2.37 ..., 9.3 0.6 1.62]\n", " [ 14.13 4.1 2.74 ..., 9.2 0.61 1.6 ]]\n" ] } ], "source": [ "\n", "X=np.c_[X1,X2,X3,X4,X5,X6, X7, X8,X9,X10,X11,X12]\n", "print(X)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ ], "source": [ "\n", "def plot_map2d(clf,XX):\n", " x_min, x_max=XX[:,0].min(), XX[:,0].max()# вычисляем мин и макс знач признака в столбце 0\n", " y_min, y_max=XX[:,1].min(), XX[:,1].max()#вычисляем мин и макс знач признака в столбце 1\n", " x_range=np.linspace(x_min,x_max,200)# создаем одномерную сетку по оси Х\n", " y_range=np.linspace(y_min,y_max,200)#создаем одномерную сетку по оси У\n", " xx, yy=np.meshgrid(x_range, y_range)#создаем двумерную сетку по двум одномерным\n", " \n", " #\n", " #np.c_[C1,C2]- создает двумерный массив который получается в результате объединения двух столбцов С1 и С2\n", " #xx.ravel(), yy.ravel() - создаем одномерное представление двухмерных сеток как конкатенацию строк сетки\n", " \n", " Z=clf.predict(np.c_[xx.ravel(), yy.ravel()])# подсказываем с помощью обученного классификатора \n", " Z= Z.reshape(xx.shape)# одномерный массив значений превращаем в двумерный с формой как у ХХ\n", " #plt.winter()\n", " plt.imshow(Z, extent=(x_min, x_max, y_min, y_max), aspect=\"auto\",\n", " interpolation=\"bilinear\", origin=\"lower\")# выводим цветовую карту " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "PCA(copy=True, iterated_power='auto', n_components=None, random_state=None,\n", " svd_solver='auto', tol=0.0, whiten=False)" ] }, "execution_count": 9, "metadata": { }, "output_type": "execute_result" } ], "source": [ "pca= decomposition.PCA()\n", "pca.fit(X)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ ], "source": [ " #plt.figure(figsize = (5,4))\n", "#plt.bar(range(178),pca.explained_variance_ratio_)\n", "#plt.show()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(178, 2)\n" ] } ], "source": [ "pca.n_components = 2\n", "U = pca.fit_transform(X)\n", "print(U.shape)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", " metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n", " weights='distance')" ] }, "execution_count": 12, "metadata": { }, "output_type": "execute_result" } ], "source": [ "clf2=neighbors.KNeighborsClassifier(n_neighbors= 5, weights = 'distance')\n", "clf2.fit(U,Y)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.0\n" ] } ], "source": [ "Y_p=clf2.predict(U)\n", "print(metrics.accuracy_score(Y, Y_p))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "00cdbad16624ad9906839d64df41e6a35d97c0a3" }, "metadata": { "image/png": { "height": 331, "width": 402 } } } ], "source": [ "plt.figure(figsize=(6.0,5.0))\n", "\n", "plot_map2d(clf2,U)\n", "\n", "plt.scatter(U[:,0],U[:,1], c=Y,edgecolors='k')\n", "\n", "plt.title('distance')\n", "plt.xlabel('1я компонента')\n", "plt.ylabel('2я компонента')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", " metric_params=None, n_jobs=1, n_neighbors=3, p=2,\n", " weights='distance')" ] }, "execution_count": 15, "metadata": { }, "output_type": "execute_result" } ], "source": [ "clf2=neighbors.KNeighborsClassifier(n_neighbors= 3, weights = 'distance')\n", "clf2.fit(U,Y)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.0\n" ] } ], "source": [ "Y_p=clf2.predict(U)\n", "print(metrics.accuracy_score(Y, Y_p))" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "79f64dfd7584332e537f904fdd830192b897f0d7" }, "metadata": { "image/png": { "height": 331, "width": 402 } } } ], "source": [ "plt.figure(figsize=(6.0,5.0))\n", "\n", "plot_map2d(clf2,U)\n", "\n", "plt.scatter(U[:,0],U[:,1], c=Y,edgecolors='k')\n", "\n", "plt.title('distance')\n", "plt.xlabel('1я компонента')\n", "plt.ylabel('2я компонента')\n", "\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n", " metric_params=None, n_jobs=1, n_neighbors=7, p=2,\n", " weights='distance')" ] }, "execution_count": 18, "metadata": { }, "output_type": "execute_result" } ], "source": [ "\n", "clf2=neighbors.KNeighborsClassifier(n_neighbors= 7, weights = 'distance')\n", "clf2.fit(U,Y)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.0\n" ] } ], "source": [ "Y_p=clf2.predict(U)\n", "print(metrics.accuracy_score(Y, Y_p))" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "e5a2864692ace0e03b3fdba10e32cfa2763b8994" }, "metadata": { "image/png": { "height": 331, "width": 402 } } } ], "source": [ "plt.figure(figsize=(6.0,5.0))\n", "\n", "plot_map2d(clf2,U)\n", "\n", "plt.scatter(U[:,0],U[:,1], c=Y,edgecolors='k')\n", "\n", "plt.title('distance')\n", "plt.xlabel('1я компонента')\n", "plt.ylabel('2я компонента')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "collapsed": false }, "outputs": [ ], "source": [ ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "collapsed": false }, "outputs": [ ], "source": [ ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "collapsed": false }, "outputs": [ ], "source": [ ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (Anaconda)", "language": "python", "name": "anaconda3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 0 }