ML4DM Lab9 - (SVM).ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"ML4DM Lab8 - (SVM).ipynb","provenance":[],"collapsed_sections":["5a5OPuWu0Gac","jrIRNTGXz5Iq","QSQoO-iM0bhl"]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"5a5OPuWu0Gac","colab_type":"text"},"source":["# Import"]},{"cell_type":"code","metadata":{"id":"WpDOp-kt0J7C","colab_type":"code","colab":{}},"source":["import numpy as np \n","import pandas as pd \n","import numpy as np\n","import scipy.io\n","from sklearn.decomposition import PCA\n","from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n","from sklearn.tree import DecisionTreeClassifier\n","from sklearn.model_selection import train_test_split\n","from sklearn.ensemble import RandomForestClassifier\n","import matplotlib.pyplot as plt\n","import os\n","from sklearn.preprocessing import StandardScaler\n","\n","from sklearn import svm\n","from sklearn import metrics\n","from mlxtend.plotting import plot_decision_regions\n","import seaborn as sn\n","import warnings\n","warnings.filterwarnings(\"ignore\")"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"jrIRNTGXz5Iq","colab_type":"text"},"source":["# Importing the dataset"]},{"cell_type":"code","metadata":{"id":"UxR-zTV-QSWG","colab_type":"code","outputId":"c8ac4701-1065-4dd2-a753-1c825b54e877","executionInfo":{"status":"ok","timestamp":1574158891754,"user_tz":-330,"elapsed":10241,"user":{"displayName":"201601422 Daiict","photoUrl":"","userId":"06333073350363496926"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["from google.colab import drive\n","drive.mount('/content/drive')\n","\n","import scipy.io\n","mat = scipy.io.loadmat('/content/drive/My Drive/Colab Notebooks/Machine Learning For Data Mining/EEG Data/S1.mat')\n"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"oX1lCQT3z9sF","colab_type":"text"},"source":["# Data Preprocessing"]},{"cell_type":"code","metadata":{"id":"XHzWSR2XSuMj","colab_type":"code","outputId":"acdebcc9-28b5-47ea-9d60-61724e5b3dfd","executionInfo":{"status":"ok","timestamp":1574158900346,"user_tz":-330,"elapsed":1141,"user":{"displayName":"201601422 Daiict","photoUrl":"","userId":"06333073350363496926"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["print(len(mat))\n","print(mat)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["11\n","{'__header__': b'MATLAB 5.0 MAT-file, Platform: MACI64, Created on: Tue Aug 22 19:12:25 2017', '__version__': '1.0', '__globals__': [], 'sub': array(['S1'], dtype='<U2'), 'Fs': array([[62.5]]), 'N': array([[32]], dtype=uint8), 'T': array([[5188]], dtype=uint16), 'exemplarLabels': array([[40, 64, 29, ..., 59,  3, 12]], dtype=uint8), 'categoryLabels': array([[4, 6, 3, ..., 5, 1, 1]], dtype=uint8), 'X_2D': array([[-0.26424164, -0.47586018, -0.47061757, ...,  0.05198565,\n","         0.08307296, -0.04543913],\n","       [ 0.0028675 , -0.04133774,  0.05108967, ...,  0.09525956,\n","        -0.00872008,  0.12494965],\n","       [-0.22358645,  0.02478356,  0.08543183, ...,  0.0938575 ,\n","         0.06906053,  0.09690079],\n","       ...,\n","       [ 0.17795506, -0.06740315,  0.09080031, ..., -0.11675177,\n","        -0.19592201, -0.16641006],\n","       [ 0.18887348,  0.17863007,  0.32982534, ...,  0.10712342,\n","         0.10700955,  0.0652873 ],\n","       [ 0.02932549, -0.05199817, -0.15791252, ..., -0.09192452,\n","        -0.04275392,  0.08084101]]), 'X_3D': array([[[-0.26424164,  0.0028675 , -0.22358645, ...,  0.17795506,\n","          0.18887348,  0.02932549],\n","        [-0.47586018, -0.04133774,  0.02478356, ..., -0.06740315,\n","          0.17863007, -0.05199817],\n","        [-0.47061757,  0.05108967,  0.08543183, ...,  0.09080031,\n","          0.32982534, -0.15791252],\n","        ...,\n","        [ 0.26171026,  0.14618344,  0.13419194, ..., -0.06100181,\n","         -0.02166311,  0.15729509],\n","        [ 0.01664864,  0.31495295,  0.09732464, ..., -0.22246297,\n","         -0.08866313, -0.03365586],\n","        [ 0.12796299,  0.19186635,  0.00895164, ..., -0.22968245,\n","         -0.05580644,  0.03110747]],\n","\n","       [[ 0.0694119 , -0.13818073, -0.14138363, ...,  0.08345749,\n","          0.17644546, -0.20745765],\n","        [-0.01213287,  0.00301473, -0.05908795, ...,  0.16343303,\n","          0.04568902, -0.12869698],\n","        [ 0.09521431,  0.06106164,  0.16744951, ...,  0.18882636,\n","          0.13860382, -0.18160391],\n","        ...,\n","        [ 0.1149582 ,  0.06243311,  0.12605291, ..., -0.02918271,\n","          0.02641676, -0.04570607],\n","        [ 0.17629073,  0.07099337,  0.17803752, ..., -0.16782443,\n","          0.07102925, -0.04762711],\n","        [ 0.12441933,  0.17428174,  0.17092345, ..., -0.17114856,\n","         -0.02164119, -0.04592316]],\n","\n","       [[-0.03701935, -0.07803175, -0.18127277, ...,  0.11560064,\n","         -0.11411913, -0.02656195],\n","        [ 0.03341169,  0.05085855, -0.06475672, ..., -0.00873957,\n","         -0.05621742,  0.07545269],\n","        [ 0.03736602,  0.07329164,  0.10168618, ..., -0.01803932,\n","          0.05661996,  0.0659789 ],\n","        ...,\n","        [-0.027258  ,  0.07248117,  0.08863233, ..., -0.17443814,\n","         -0.03109104, -0.1193728 ],\n","        [ 0.0365947 ,  0.03080346,  0.04844074, ..., -0.25368436,\n","         -0.06902409, -0.07272953],\n","        [-0.01021573,  0.12006075,  0.07405634, ..., -0.32575964,\n","         -0.0868055 , -0.00740015]],\n","\n","       ...,\n","\n","       [[ 0.0312043 , -0.05747297, -0.00911503, ...,  0.08848574,\n","          0.11227239, -0.07619217],\n","        [-0.04661125,  0.06127449,  0.1098959 , ...,  0.04420064,\n","          0.0718102 , -0.04163834],\n","        [ 0.02757448,  0.06193236,  0.2294212 , ...,  0.09504399,\n","          0.13624097, -0.09169688],\n","        ...,\n","        [ 0.13513299,  0.06478127,  0.04771938, ..., -0.08105062,\n","          0.11105604,  0.03617733],\n","        [ 0.09274329,  0.11914563,  0.03327877, ..., -0.15689004,\n","          0.10180986, -0.07421863],\n","        [ 0.11383087,  0.09816807,  0.06208975, ..., -0.14178735,\n","          0.04711402, -0.08828135]],\n","\n","       [[ 0.00563484, -0.06499   , -0.15210425, ...,  0.04965808,\n","         -0.0042287 , -0.05827923],\n","        [-0.05271585,  0.0147073 , -0.04961749, ...,  0.00808789,\n","          0.02755416,  0.0259631 ],\n","        [-0.05393718, -0.0362699 ,  0.10793566, ...,  0.05622378,\n","          0.07613327, -0.07331583],\n","        ...,\n","        [ 0.03119056,  0.1949324 ,  0.14322966, ..., -0.09833201,\n","          0.16318681, -0.04267162],\n","        [ 0.07318838,  0.1459948 ,  0.11655193, ..., -0.12811501,\n","          0.14241894, -0.03410432],\n","        [ 0.07352434,  0.17163425,  0.1237841 , ..., -0.14529347,\n","          0.04917896, -0.03223843]],\n","\n","       [[-0.02382347, -0.01895111, -0.17685526, ...,  0.06243312,\n","         -0.08120851, -0.08553067],\n","        [-0.01343152,  0.01229406, -0.09517451, ..., -0.00257212,\n","         -0.00555704,  0.08262864],\n","        [-0.01428892,  0.04097209,  0.04658644, ...,  0.05022952,\n","          0.05072851,  0.00500705],\n","        ...,\n","        [ 0.05198565,  0.09525956,  0.0938575 , ..., -0.11675177,\n","          0.10712342, -0.09192452],\n","        [ 0.08307296, -0.00872008,  0.06906053, ..., -0.19592201,\n","          0.10700955, -0.04275392],\n","        [-0.04543913,  0.12494965,  0.09690079, ..., -0.16641006,\n","          0.0652873 ,  0.08084101]]])}\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Oq-h1_nSUY_g","colab_type":"code","outputId":"685e71d4-98d5-4e77-b9b2-ad8868c58d8c","executionInfo":{"status":"ok","timestamp":1574158910617,"user_tz":-330,"elapsed":1133,"user":{"displayName":"201601422 Daiict","photoUrl":"","userId":"06333073350363496926"}},"colab":{"base_uri":"https://localhost:8080/","height":660}},"source":["df=pd.DataFrame.from_dict(mat,orient='index')\n","\n","print(df.head())\n","\n","data=df[0].X_3D\n","print(\"------------------\")\n","#print(data)\n","print(\"------------------\")\n","\n","data=data.transpose(1,2,0)\n","\n","\n","print(\"------------------\")\n","#data=data.reshape(5188,3968)\n","print(data.shape)\n","print(data[0])\n","print(\"------------------\")\n","\n","label=df[0].categoryLabels\n","\n","y=np.transpose(label)\n","\n","y=y-1\n","\n","print(\"------------------\")\n","print(\"y: \",y.shape)\n","print(y)\n","print(\"------------------\")\n","\n","print(len(data))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["                                                             0\n","__header__   b'MATLAB 5.0 MAT-file, Platform: MACI64, Creat...\n","__version__                                                1.0\n","__globals__                                                 []\n","sub                                                       [S1]\n","Fs                                                    [[62.5]]\n","------------------\n","------------------\n","------------------\n","(32, 5188, 124)\n","[[-0.26424164  0.0694119  -0.03701935 ...  0.0312043   0.00563484\n","  -0.02382347]\n"," [ 0.0028675  -0.13818073 -0.07803175 ... -0.05747297 -0.06499\n","  -0.01895111]\n"," [-0.22358645 -0.14138363 -0.18127277 ... -0.00911503 -0.15210425\n","  -0.17685526]\n"," ...\n"," [ 0.17795506  0.08345749  0.11560064 ...  0.08848574  0.04965808\n","   0.06243312]\n"," [ 0.18887348  0.17644546 -0.11411913 ...  0.11227239 -0.0042287\n","  -0.08120851]\n"," [ 0.02932549 -0.20745765 -0.02656195 ... -0.07619217 -0.05827923\n","  -0.08553067]]\n","------------------\n","------------------\n","y:  (5188, 1)\n","[[3]\n"," [5]\n"," [2]\n"," ...\n"," [4]\n"," [0]\n"," [0]]\n","------------------\n","32\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"wPI-CF4j0TgT","colab_type":"text"},"source":["# PCA Function"]},{"cell_type":"code","metadata":{"id":"qwh8DsZmNadH","colab_type":"code","colab":{}},"source":["def do_pca(input_data, variance):\n","  #print(\"-----------------------------------------------------------------------------------------------------------------------\")\n","  #print()\n","  #print(\"initial shape of data: \",input_data.shape)\n","\n","  scaler = StandardScaler()\n","  input_data = scaler.fit_transform(input_data)\n","\n","  pca = PCA(n_components = variance)\n","  principalComponents = pca.fit_transform(input_data)\n","\n","  #print(\"Shape of data after PCA: \",principalComponents.shape)\n","\n","  #print(\"PCA explained varience ratio: \",pca.explained_variance_ratio_)\n","  #plt.plot(pca.explained_variance_ratio_)\n","  #plt.title('PCA Explained Variance Ratio')\n","  #plt.grid(True,which='major',axis='both', linestyle = '-')\n","  #plt.grid(True,which='minor',axis='both', linestyle = '--')\n","  #plt.minorticks_on()\n","  #plt.show()\n","\n","  #print(\"-----------------------------------------------------------------------------------------------------------------------\")\n","\n","  return principalComponents"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"QSQoO-iM0bhl","colab_type":"text"},"source":["#--"]},{"cell_type":"code","metadata":{"id":"VOdGfgzdObqv","colab_type":"code","outputId":"0922725c-57e1-4fbe-af9d-b3b81d303177","executionInfo":{"status":"ok","timestamp":1574158973864,"user_tz":-330,"elapsed":2061,"user":{"displayName":"201601422 Daiict","photoUrl":"","userId":"06333073350363496926"}},"colab":{"base_uri":"https://localhost:8080/","height":971}},"source":["\n","principalComponent = do_pca(data[0],2)\n","\n","print(\"pca_comp: \",principalComponent.shape)\n","print(\"y: \",y.shape)\n","plt.scatter(principalComponent[:,0],principalComponent[:,1])\n","plt.show()\n","\n","y = pd.DataFrame(data = y, columns=['target'])\n","principalDf = pd.DataFrame(data = principalComponent, columns = ['principal_component_1', 'principal_component_2'])\n","finalDf = pd.concat([principalDf, y], axis = 1)\n","\n","print(finalDf)\n","'''\n","\n","zero_label = finalDf.loc[finalDf['target'] == 0]\n","zero_label.pop('target')\n","\n","one_label = finalDf.loc[finalDf['target'] == 1]\n","one_label.pop('target')\n","\n","two_label = finalDf.loc[finalDf['target'] == 2]\n","two_label.pop('target')\n","\n","fig = plt.figure()\n","ax1 = fig.add_subplot(111)\n","\n","ax1.scatter(zero_label.iloc[:,0],zero_label.iloc[:,1],c='r')\n","ax1.scatter(one_label.iloc[:,0],one_label.iloc[:,1],c='b')\n","ax1.scatter(two_label.iloc[:,0],two_label.iloc[:,1],c='g')\n","plt.show()\n","'''"],"execution_count":0,"outputs":[{"output_type":"stream","text":["-----------------------------------------------------------------------------------------------------------------------\n","\n","initial shape of data:  (5188, 124)\n","Shape of data after PCA:  (5188, 2)\n","PCA explained varience ratio:  [0.24463251 0.1611702 ]\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXoAAAEICAYAAABRSj9aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["-----------------------------------------------------------------------------------------------------------------------\n","pca_comp:  (5188, 2)\n","y:  (5188, 1)\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXkAAAD4CAYAAAAJmJb0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYwryIiI5piAvIpJj/x+AsD4C0fnQsAAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["      principal_component_1  principal_component_2  target\n","0                  2.670374               1.504946       3\n","1                  2.734290              -4.452583       5\n","2                  2.140779              -9.317932       2\n","3                  3.720886               0.464374       5\n","4                  6.128821              -0.257498       3\n","...                     ...                    ...     ...\n","5183               1.638606               2.204960       5\n","5184              -4.338746               5.752922       4\n","5185              -2.246629               6.599859       4\n","5186               5.027574               3.541564       0\n","5187               4.068451              -5.141806       0\n","\n","[5188 rows x 3 columns]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["\"\\n\\nzero_label = finalDf.loc[finalDf['target'] == 0]\\nzero_label.pop('target')\\n\\none_label = finalDf.loc[finalDf['target'] == 1]\\none_label.pop('target')\\n\\ntwo_label = finalDf.loc[finalDf['target'] == 2]\\ntwo_label.pop('target')\\n\\nfig = plt.figure()\\nax1 = fig.add_subplot(111)\\n\\nax1.scatter(zero_label.iloc[:,0],zero_label.iloc[:,1],c='r')\\nax1.scatter(one_label.iloc[:,0],one_label.iloc[:,1],c='b')\\nax1.scatter(two_label.iloc[:,0],two_label.iloc[:,1],c='g')\\nplt.show()\\n\""]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"code","metadata":{"id":"CAMMcGCzYu0f","colab_type":"code","outputId":"c3a72818-8bcb-42ff-8b3f-0138c7d87e65","executionInfo":{"status":"ok","timestamp":1574158991834,"user_tz":-330,"elapsed":1588,"user":{"displayName":"201601422 Daiict","photoUrl":"","userId":"06333073350363496926"}},"colab":{"base_uri":"https://localhost:8080/","height":575}},"source":["principalComponent = do_pca(data[7],0.95)\n","\n","print(\"pca_comp: \",principalComponent.shape)\n","print(\"y: \",y.shape)\n","\n"],"execution_count":0,"outputs":[{"output_type":"stream","text":["-----------------------------------------------------------------------------------------------------------------------\n","\n","initial shape of data:  (5188, 124)\n","Shape of data after PCA:  (5188, 51)\n","PCA explained varience ratio:  [0.26367633 0.16355787 0.13826054 0.07371073 0.04426266 0.03050999\n"," 0.02614982 0.02025615 0.01810239 0.01323773 0.01279836 0.00974588\n"," 0.00913423 0.00833288 0.00731779 0.00614679 0.00588407 0.00551245\n"," 0.00535925 0.0051478  0.00443748 0.00432996 0.0041313  0.00380811\n"," 0.00377902 0.00359471 0.00352073 0.00335118 0.003114   0.00294007\n"," 0.00285198 0.00277749 0.00271748 0.00258504 0.00252591 0.00249356\n"," 0.00243614 0.00230789 0.00228522 0.00217771 0.00212917 0.00209148\n"," 0.00206108 0.00200031 0.00196296 0.00195348 0.00184375 0.00183624\n"," 0.00180752 0.00179175 0.00176458]\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXoAAAEICAYAAABRSj9aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["-----------------------------------------------------------------------------------------------------------------------\n","pca_comp:  (5188, 51)\n","y:  (5188, 1)\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"6n_EHn2b0edY","colab_type":"text"},"source":["# RBF SVC"]},{"cell_type":"code","metadata":{"id":"IBMYqkU7YEOr","colab_type":"code","outputId":"e54714c6-6c2d-48ea-9fe2-6621c2d6fd28","executionInfo":{"status":"ok","timestamp":1574161700095,"user_tz":-330,"elapsed":162016,"user":{"displayName":"201601422 Daiict","photoUrl":"","userId":"06333073350363496926"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["#linear_score = []\n","rbf_score = []\n","#poly_score = []\n","#sigmoid_score = []\n","\n","for i in range(0,32):\n","  print(\"Time Stamp: \",i+1)\n","  principalComponent = do_pca(data[i],0.95)\n","  X_train, X_test, y_train, y_test = train_test_split(principalComponent, y, test_size=0.1)\n","\n","  X_train=(X_train-X_train.mean())/X_train.std()\n","  X_test=(X_test-X_test.mean())/X_test.std()\n","\n","  rbfsvmc = svm.SVC(kernel = 'rbf', gamma = 0.5)\n","  rbf_svmc = rbfsvmc.fit(X_train, y_train)\n","  rbf_y_pred = rbfsvmc.predict(X_test)\n","  rbf_score.append(rbfsvmc.score(X_test,y_test))\n","  print(\"Score :: SVM -> Kernel = rbf: \",rbf_score[i])\n","\n","  #rbf_confusion_matrix = metrics.confusion_matrix(y_test,rbf_y_pred)\n","  #print(\"RBF Confusion Matrix: \")\n","  #print(rbf_confusion_matrix)\n","  #plt.show(sn.heatmap(rbf_confusion_matrix, annot=True))\n","\n","'''\n","  linearsvmc = svm.SVC(kernel = 'linear')\n","  linear_svmc = linearsvmc.fit(X_train, y_train)\n","  linear_y_pred = linearsvmc.predict(X_test)\n","  linear_score.append(linearsvmc.score(X_test,y_test))\n","  print(\"Score :: SVM -> Kernel = linear: \",linear_score[i])\n","  #linear_confusion_matrix = metrics.confusion_matrix(y_test,linear_y_pred)\n","  #print(\"Linear Confusion Matrix: \")\n","  #print(linear_confusion_matrix)\n","  #plt.show(sn.heatmap(linear_confusion_matrix, annot=True))\n","'''\n","\n","  \n","'''\n","  polysvmc = svm.SVC(kernel = 'poly', degree = 2)\n","  poly_svmc = polysvmc.fit(X_train, y_train)\n","  poly_y_pred = polysvmc.predict(X_test)\n","  poly_score.append(linearsvmc.score(X_test,y_test))\n","  print(\"Score :: SVM -> Kernel = poly: \",poly_score[i])\n","  #poly_confusion_matrix = metrics.confusion_matrix(y_test,poly_y_pred)\n","  #print(\"Poly Confusion Matrix: \")\n","  #print(poly_confusion_matrix)\n","  #plt.show(sn.heatmap(poly_confusion_matrix, annot=True))\n","\n","  sigmoidsvmc = svm.SVC(kernel = 'sigmoid')\n","  sigmoid_svmc = sigmoidsvmc.fit(X_train, y_train)\n","  sigmoid_y_pred = sigmoidsvmc.predict(X_test)\n","  sigmoid_score.append(linearsvmc.score(X_test,y_test))\n","  print(\"Score :: SVM -> Kernel = sigmoid: \",sigmoid_score[i])\n","  #sigmoid_confusion_matrix = metrics.confusion_matrix(y_test,sigmoid_y_pred)\n","  #print(\"Sigmoid Confusion Matrix: \")\n","  #print(sigmoid_confusion_matrix)\n","  #plt.show(sn.heatmap(sigmoid_confusion_matrix, annot=True))\n","'''\n"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Time Stamp:  1\n","Score :: SVM -> Kernel = rbf:  0.1599229287090559\n","Time Stamp:  2\n","Score :: SVM -> Kernel = rbf:  0.1464354527938343\n","Time Stamp:  3\n","Score :: SVM -> Kernel = rbf:  0.12716763005780346\n","Time Stamp:  4\n","Score :: SVM -> Kernel = rbf:  0.1579961464354528\n","Time Stamp:  5\n","Score :: SVM -> Kernel = rbf:  0.15221579961464354\n","Time Stamp:  6\n","Score :: SVM -> Kernel = rbf:  0.14258188824662812\n","Time Stamp:  7\n","Score :: SVM -> Kernel = rbf:  0.14258188824662812\n","Time Stamp:  8\n","Score :: SVM -> Kernel = rbf:  0.17148362235067438\n","Time Stamp:  9\n","Score :: SVM -> Kernel = rbf:  0.13102119460500963\n","Time Stamp:  10\n","Score :: SVM -> Kernel = rbf:  0.13872832369942195\n","Time Stamp:  11\n","Score :: SVM -> Kernel = rbf:  0.16570327552986513\n","Time Stamp:  12\n","Score :: SVM -> Kernel = rbf:  0.15414258188824662\n","Time Stamp:  13\n","Score :: SVM -> Kernel = rbf:  0.15414258188824662\n","Time Stamp:  14\n","Score :: SVM -> Kernel = rbf:  0.13872832369942195\n","Time Stamp:  15\n","Score :: SVM -> Kernel = rbf:  0.13102119460500963\n","Time Stamp:  16\n","Score :: SVM -> Kernel = rbf:  0.1233140655105973\n","Time Stamp:  17\n","Score :: SVM -> Kernel = rbf:  0.13680154142581888\n","Time Stamp:  18\n","Score :: SVM -> Kernel = rbf:  0.1464354527938343\n","Time Stamp:  19\n","Score :: SVM -> Kernel = rbf:  0.1464354527938343\n","Time Stamp:  20\n","Score :: SVM -> Kernel = rbf:  0.14836223506743737\n","Time Stamp:  21\n","Score :: SVM -> Kernel = rbf:  0.1348747591522158\n","Time Stamp:  22\n","Score :: SVM -> Kernel = rbf:  0.15028901734104047\n","Time Stamp:  23\n","Score :: SVM -> Kernel = rbf:  0.16377649325626203\n","Time Stamp:  24\n","Score :: SVM -> Kernel = rbf:  0.13872832369942195\n","Time Stamp:  25\n","Score :: SVM -> Kernel = rbf:  0.14258188824662812\n","Time Stamp:  26\n","Score :: SVM -> Kernel = rbf:  0.15221579961464354\n","Time Stamp:  27\n","Score :: SVM -> Kernel = rbf:  0.14258188824662812\n","Time Stamp:  28\n","Score :: SVM -> Kernel = rbf:  0.15221579961464354\n","Time Stamp:  29\n","Score :: SVM -> Kernel = rbf:  0.15221579961464354\n","Time Stamp:  30\n","Score :: SVM -> Kernel = rbf:  0.15606936416184972\n","Time Stamp:  31\n","Score :: SVM -> Kernel = rbf:  0.14065510597302505\n","Time Stamp:  32\n","Score :: SVM -> Kernel = rbf:  0.13102119460500963\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["'\\n  polysvmc = svm.SVC(kernel = \\'poly\\', degree = 2)\\n  poly_svmc = polysvmc.fit(X_train, y_train)\\n  poly_y_pred = polysvmc.predict(X_test)\\n  poly_score.append(linearsvmc.score(X_test,y_test))\\n  print(\"Score :: SVM -> Kernel = poly: \",poly_score[i])\\n  #poly_confusion_matrix = metrics.confusion_matrix(y_test,poly_y_pred)\\n  #print(\"Poly Confusion Matrix: \")\\n  #print(poly_confusion_matrix)\\n  #plt.show(sn.heatmap(poly_confusion_matrix, annot=True))\\n\\n  sigmoidsvmc = svm.SVC(kernel = \\'sigmoid\\')\\n  sigmoid_svmc = sigmoidsvmc.fit(X_train, y_train)\\n  sigmoid_y_pred = sigmoidsvmc.predict(X_test)\\n  sigmoid_score.append(linearsvmc.score(X_test,y_test))\\n  print(\"Score :: SVM -> Kernel = sigmoid: \",sigmoid_score[i])\\n  #sigmoid_confusion_matrix = metrics.confusion_matrix(y_test,sigmoid_y_pred)\\n  #print(\"Sigmoid Confusion Matrix: \")\\n  #print(sigmoid_confusion_matrix)\\n  #plt.show(sn.heatmap(sigmoid_confusion_matrix, annot=True))\\n'"]},"metadata":{"tags":[]},"execution_count":30}]},{"cell_type":"code","metadata":{"id":"qEi-_DJnTU6A","colab_type":"code","outputId":"41a5fb2a-83d8-4965-f9fc-570017f2802c","executionInfo":{"status":"ok","timestamp":1574161756337,"user_tz":-330,"elapsed":2447,"user":{"displayName":"201601422 Daiict","photoUrl":"","userId":"06333073350363496926"}},"colab":{"base_uri":"https://localhost:8080/","height":283}},"source":["plt.plot(rbf_score)\n","plt.show()\n","print(\"Mean Score RBF: \", np.mean(rbf_score))\n"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXoAAAD4CAYAAADiry33AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["Mean Score RBF:  0.14601396917148363\n"],"name":"stdout"}]}]}