run_demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.io as sio\n",
    "import numpy as np\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn import svm\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import LeaveOneOut\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from sklearn.decomposition import PCA\n",
    "from helpers.simulateData import *\n",
    "from helpers.fs_methods import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "kf5=KFold(n_splits=5,shuffle=True)\n",
    "kf10=KFold(n_splits=10,shuffle=True)\n",
    "loo=LeaveOneOut()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulate Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 41 AD and 36 LMCI with 35 ROIs "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Select the number of class 1 graphs:  41\n",
      "Select the number of class 2 graphs:  36\n",
      "Select the number of nodes (i.e., ROIS for brain graphs):  35\n"
     ]
    }
   ],
   "source": [
    "#* * (1) Some variables are described.\n",
    "mu1 = 0.9 #Mean value of the first Gaussian distribution\n",
    "sigma1 = 0.4 #Standard deviation value of the first Gaussian distribution\n",
    "mu2 = 0.7 #Mean value of the second Gaussian distribution\n",
    "sigma2 = 0.6 #Standard deviation value of the first Gaussian distribution\n",
    "#Nf = 5 #number of selected features\n",
    "Featurematrix,Subjects,Labels=simulate_data(mu1,sigma1,mu2,sigma2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=Featurematrix\n",
    "y=Labels\n",
    "y=y[:, 0]\n",
    "n_samples, n_features = X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
       "        1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
       "        1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,\n",
       "        1.,  1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1.,\n",
       "       -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1.,\n",
       "       -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1., -1.])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(77, 595)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of selected features (top k ranked features)\n",
    "#top K selected features varying from 10 to 100 (with a step size of 10 features)\n",
    "num_fea = [i for i in range(10,110,10)]\n",
    "clf = svm.LinearSVC()    # linear SVM\n",
    "correct=0\n",
    "accuracy=[]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature selection methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vizualize classification results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "  #   X_train, X_test = X[train_index], X[test_index]\n",
    "k=10\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n",
    "     #   y_train, y_test = y[train_index], y[test_index]\n",
    "#if FS_method==reliefF:\n",
    "idx,score=relief_FS(X_train,y_train)\n",
    "\n",
    "selected_features_train = X_train[:, idx]\n",
    "selected_features_test = X_test[:, idx]\n",
    "    # train a classification model with the selected features on the training dataset\n",
    "clf.fit(selected_features_train, y_train)  # predict the class labels of test data\n",
    "y_predict = clf.predict(selected_features_test)\n",
    "    # obtain the classification accuracy on the test data\n",
    "acc = accuracy_score(y_test, y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0 10]\n",
      " [ 0 16]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        -1.0       0.00      0.00      0.00        10\n",
      "         1.0       0.62      1.00      0.76        16\n",
      "\n",
      "    accuracy                           0.62        26\n",
      "   macro avg       0.31      0.50      0.38        26\n",
      "weighted avg       0.38      0.62      0.47        26\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/raoua/.local/lib/python3.6/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(confusion_matrix(y_test,y_predict))\n",
    "print(classification_report(y_test,y_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=2).fit(selected_features_train)\n",
    "pca_2d = pca.transform(selected_features_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.11281939,  0.21235363, -0.09401972,  1.26474487,  0.31267855,\n",
       "       -3.08098487, -3.19207001, -0.10790115,  0.49926259,  0.82599513,\n",
       "       -2.03396602, -0.35663944,  3.66258588, -0.67626191,  0.48933298,\n",
       "        0.79690841, -0.39197119, -2.02112748,  0.03102639,  0.26806968,\n",
       "       -2.40206023, -0.37382329,  5.61343377, -3.98872559,  0.95990189,\n",
       "       -0.0140498 , -0.6844814 ,  0.1090563 ,  0.71898405,  0.61510839,\n",
       "       -0.02066971, -1.43817182,  0.10219127, -0.46679235,  0.01626537,\n",
       "        4.73191082, -1.81763045, -1.8317156 , -0.25769955, -0.83220324,\n",
       "        0.06884404,  0.6549621 ,  3.26215995,  0.7202894 , -0.08913708,\n",
       "       -0.53676008,  0.16948951, -1.15943891,  1.57509098,  0.53607286,\n",
       "       -0.46123734])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_2d[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components=2).fit(selected_features_train)\n",
    "pca_2d = pca.transform(selected_features_train)\n",
    "import pylab as pl\n",
    "for i in range(0, pca_2d.shape[0]):\n",
    "    if y_train[i] == -1:\n",
    "        c1 = pl.scatter(pca_2d[i,0],pca_2d[i,1],c='r',    marker='+')\n",
    "    elif y_train[i] == 1:\n",
    "        c2 = pl.scatter(pca_2d[i,0],pca_2d[i,1],c='b',    marker='o')\n",
    "\n",
    "pl.legend([c1, c2], ['AD', 'MCI'])\n",
    "pl.title('Linear SVM classifier without Feature selection')\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jqenoUdL6vKPeAiLiIdI53FtI77P6ezrQz77Ren2K1KHrs6QEeiD975eWk5qCf54brvs9iYgHSIni11lMA23On0A63/8b0vqbTWo1abZpcSCf/1+Q9htPA58gfW41RcTPSR2bLiPtc5fTt2K7jlQ5f6uZAyJlJ7DMzMxKxxcmmplZaTlJmZlZaTlJmZlZaTlJmZlZaZXthqJNmTRpUkyfPr3oMMzMOsrKlSufiojJRccxEB2ZpKZPn86KFSv6n9HMzLaT1OhOEaXk5j4zMystJykzMystJykzMyutjjwnVcsLL7zA+vXr2bJlS9GhtNTYsWOZMmUKY8aMKToUM7NhN2KS1Pr165kwYQLTp08n3au180UEGzduZP369ey7775Fh2NmNuxGTHPfli1bmDhx4ohJUACSmDhx4oirDs3MmjVikhQwohJUxUh8T2ZmzRpRScrMrOPNmpUeBjhJtcV3v/tdJPHAAw8AsGbNGsaNG8eMGTM46KCDOPLII7n22muLDdLMrAOMmI4TZdLb28txxx1Hb28vF110EQD7778/d999NwC//vWvedvb3kZEcOaZZxYZqpmVRaV6Wr687/CyZQUEUx7dXUm1oazetGkTt99+O1dffTXXX399zXn2228/Lr30Ur785S+3dNlmZiONK6kW+973vsfs2bN51atexcSJE1m5ciUTJ07cab7DDz98e3Ogmdn2iskVVB/dWUlVKqjly9OjhRVVb28vc+fOBWDu3Ln09vbWnC8iWrI8M7ORzJVUCz399NP8+Mc/5he/+AWS2LZtG5L44Ac/uNO8d999NwcddFABUZpZqbmC6qM7k1SbyuobbriB008/nSuvvHL7uJkzZ7Ju3bo+861Zs4bzzz+fD33oQy1ZrpnZSNWdSapNent7ueCCC/qMe/vb384ll1zCww8/zIwZM9iyZQsTJkzgnHPO4YwzzigmUDOzDqFOPDfS09MT1T96uHr16hHbfDaS35uZDR9JKyOip+g4BqI7O06YmVlHcJIyM7PScpIyM7PScpIyM7PScpIyM7PScpIyM7PScpJqIUmcdtpp24e3bt3K5MmTmTNnzvZxN910Ez09PRx88MHMmDGD8847D4BFixbxxS9+cdhjNjMrMyepFtptt91YtWoVzz33HABLly5l77333j591apVnH322SxevJj777+fFStWcMABBxQVrplZ6XVtklqyBKZPh1Gj0t8lS1rzuieeeCI/+MEPgHQHilNPPXX7tM9//vMsXLiQ17zmNQCMHj2a97///a1ZsJnZCNSVSWrJEpg/H9auhYj0d/781iSquXPncv3117Nlyxbuu+8+jjrqqO3TVq1axRFHHDH0hZiZdYmuTFILF8LmzX3Hbd6cxg/VoYceypo1a+jt7eXEE08c+guamXWxrkxSjzwysPEDdfLJJ3P++ef3aeoDOOSQQ1i5cmVrFmJmw6sNv+Rt/StVkpI0WtLdkm5s53KmTh3Y+IE666yz+MQnPsHrXve6PuM/8pGP8JnPfIaHHnoIgBdffJErrriiNQs1MxuByvZTHecCq4GXtnMhF1+czkHlm/zGj0/jW2HKlCmcc845O40/9NBDufzyyzn11FPZvHkzkvp0TzezEqpUT8uX9x32jxMOi9IkKUlTgJOAi4G/buey5s1LfxcuTE18U6emBFUZP1ibNm3aadysWbOYlWsimDNnTs3EtGjRoqEt3MxsBCpNkgIuBz4KTKg1UdJ8YD7A1Ba0y82bN/SkZGZdoE2/5G3NKcU5KUlzgCcjom6vgoi4KiJ6IqJn8uTJwxidmdkQudPFoJWlkjoWOFnSicBY4KWSFkfEaf08r4+IQFJbAixKJ/5ystmI5AqqEKVIUhGxAFgAIGkWcP5AE9TYsWPZuHEjEydOHDGJKiLYuHEjY8eOLToUMxsMd7oYslIkqVaYMmUK69evZ8OGDUWH0lJjx45lypQpRYdhZlaI0iWpiFgGLBvo88aMGcO+++7b8njMzAbNnS6GrBQdJ8zMzGopXSVlZjbiuIIaNFdSZmZWWk5SZmZWWk5SZmZWWk5SZmZWWk5SZmZWWk5SZmZWWk5SZmZWWk5SZmZWWk5SZmZWWk5SZtY5/LtMXcdJyszMSsv37jOz8vPvMnUtV1JmZlZarqTMrPz8u0xdy5WUmZmVlispM+scrqC6jispMzMrLScpMzMrLScpMzMrLScpMzMrLScpM+suvrVSR3GSMjOz0nIXdDPrDr61UkdyJWVmZqXlSsrMuoNvrdSRXEmZmVlpuZIys+7iCqqjuJIyM7PScpIyM7PScpIyM7PSKkWSkrSPpFsl3S/pl5LOLTomMzMrXlk6TmwFzouIuyRNAFZKWhoR9xcdmJmZFacUlVREPBYRd2X/PwusBvYuNiozMytaKZJUnqTpwAzg36rGz5e0QtKKDRs2FBGamZkNs1IlKUm7A/8EfDgifpefFhFXRURPRPRMnjy5mADNzGxYlSZJSRpDSlBLIuLbRcdjZmbFK0WSkiTgamB1RFxadDxmZlYOpUhSwLHA6cAbJN2TPU4sOigzMytWKbqgR8TtgIqOw8zMyqUslZSZmdlOnKTMzKy0nKTMzKy0nKTMzKy0nKTMzKy0nKTMzKy0nKTMzCpmzUoPKw0nKTMzK61SXMxrZlaoSvW0fHnf4WXLCgjG8lxJmVnncbNc13AlZWZWqZhcQZWOk5SZdQ43y3UdJykzswonu9LxOakWWLIEpk+HUaPS3yVLio7IbIRatiw9Zs5Mj8qwjViupIZoyRKYPx82b07Da9emYYB584qLy8xsJHAlNUQLF+5IUBWbN6fxULvKcuVlNkSuoLqGK6kheuSR+uNrVVlnnQUR8MILO8a58jIzq82V1BBNnVp/fK0q6/nndySoinzlZWZmOzhJDdHFF8P48X3HjR+fxtersmoZyLzgJkMz6w5OUkM0bx5cdRVMmwZS+nvVVWl8vSqrloHMW2lGXLs2NR1WmgydqMxspHGSaoF582DNGnjxxfS3cm6pVpUFqfrJq1RetdSqmPrrrGFmNlI4SbXRvHnw7nenCitvl11g4sSdK69q9SqmtWtrL28gTYZuLjSzTuDefW32wx+mBJP3/POw++7w1FONn1uvYho9GrZt23n+ZpsMfW2XmXUKV1Jt1qiL+mCfu21b/c4azXBzoXUl3zm9IzlJtVm96mbUqP6b2uo9t9JEWKuzRjOGkjjNbJh1eXJ1kmqzep0ntm3rv2deo+7t9TprNKPRtV1mI05lJ798eXp0+U6/0zhJtVl1F/XRo3eep15TW6Pu7UPRKPmZWT+GK8k5uQLuODEs5s3bkViqu59X1Gtqqzxv4cI0TyWZDSVRVb/m1Kk7qjOzliv6N5/8g4YdzUlqACrXKA1lxz51au0u5PWa2trVEy+fOM2sCcP9g4tOroCb+5rWqrs8NNvUVrmO6bTT3BPPOlTZmqt85/SO5CTVpFZ12653ngl2XFw7aRKceWb9i3Zh8D3xfBGv2SAN5gcXW5GYuzy5Okn1o7JTb+VdHk4/PQ1//eupZx70rdI2btz5TunVBtMTz/f8s2HlX9G1FihNkpI0W9KDkn4l6WNFxwN9d+r1NJssPvCBlJxqJYhaVVp/Nm0aeHIZajXoKsyMgVVQZWnq7GCl6DghaTTw98CbgPXAnZK+HxH3FxlXf8mj2W7bS5bAFVfsfHukSoIYTNPdxo0D70AxlIt4fSslGzRXTwPT5R0lqimq95xFBCEdAyyKiD/LhhcARMQltebv6emJFStWtD2uUaN2TiwV06Y137uvUXNh5bUaTYd0/qpWLNOm7WgyHGwczbzGUJ5r1rUGk3DamKQkrYyInpa/cBuVpblvb2Bdbnh9Nm47SfMlrZC0YsOGDcMSVKPbEg3kLg+NKpXRo2v3+Nt11753Sq+XLAdShQ3lIl7fSsmszdxEWFNZklS/IuKqiOiJiJ7JkycPyzJbdWeGRuettm2r3ePvmmvSXdIrtz2aNm3gr11tKHew8K2UzAbBnUWGLiIKfwDHAD/KDS8AFtSb/4gjjojhsnhxxLRpEVL6u3jx4F5Dikj1UN/HtGnNv8b48X2fO3784OIZjKKXb9Y1Zs5Mj3rDQwCsiBLs8wfyKEsldSdwoKR9Je0KzAW+X3BMwNBu5Jp/jfe9b+cfPxxIVdau+/g1q+jlWxdxM5fllKLjBICkE4HLgdHANRFRd/c9XB0nBqKZWya14rZKZiOee7cl1bdhmjkz/R3CenHHiSGIiB9GxKsiYv9GCapVWnnNT7MXybaiKmsFX+9kpeSOA1ZDKa6TGm6tvuan0UWyZauUfL2TWYfwDWaBEjX3DcRQm/tafc1PveuppFQ1lYmvd7LS6/Kd8k5auD46sbmvKyupVl/zM9Cf3yiSr3cy6zBdnqxLc05qOLX6mp9a11ONGZPur1e28z6+3slKz9cWWU5XJqlW/3x6dffsyp0iNm4c3N3G29mxwT8db2adpCuTVDuu+cn33Nt9d3j++b7Tm73beLt/TsPXO5lZJ+nKjhPtNpSOFO7YYGbt0okdJ7qykmq3oZz3cccGM7MdnKTaYCjnfdyxwcxsByepNhjKeR93bDAz26Err5MaDvPmDf5mtOB7/JkNiS8IHjFcSZVAdZdzKMc9/szMiuZKqmC+l55ZC1XfOdwVVcdzJVWwRjenNTPrdq6kCuYu52Yt5DuHjziupArmLudmI5R/D6slnKQK5i7nZm3gm9SOGG7uK5i7nJuNMO680VJOUiUw2GuqzMxGOicpM7NWcueNlvI5KTMzKy1XUmZm7eAKqiVcSZlZZ3CX7q7kJGVmZqXl5j4zKzd36e5qrqTMrLu42bCjuJIys3IbSpduV10dz0nKzLqDmw07kpOUmXWGwVRQ+YR0zz1w2GEtDsrazUnKzLrDYYelRNeOCspVWds4SZnZyONbE40YTlJm1l3aUUH5PFfbFJ6kJH0BeAvwPPAwcGZEPFNsVGY2IjhZdLzCkxSwFFgQEVslfQ5YAFxQcExmZv1zs2LbFX4xb0TcHBFbs8E7gClFxmNmZuVRhkoq7yzgG0UHYWY2IK6g2mZYkpSkW4BX1Ji0MCK+l82zENgKLKnzGvOB+QBTp05tU6RmZkPkpr+WGpYkFRHHN5ou6QxgDvDGiIg6r3EVcBVAT09PzXnMzGxkKby5T9Js4KPAzIjYXHQ8ZmaD4u7obVF4xwngK8AEYKmkeyRdUXRAZmZWDoVXUhFxQNExmJkNmbujt0UZKikzM7OaCq+kzMxGFFdQLeVKyszMSstJyszMSstJyszMSstJyszMSstJyszMSstJyszMSstJysxsqGbN2nERr7WUk5SZmZWWL+Y1Mxss31S27VxJmZlZabmSMjMbLN9Utu1cSZmZWWm5kjIzGypXUG3jSsrMzErLScrMzErLScrMzErLScrMzErLScrMzErLScrMzErLScrMzErLScrMWmugdwT3HcStAScpMzMrLd9xwsxaY6B3BPcdxK0JrqTMzKy0XEmZWWsM9I7gvoO4NcGVlJmZlZYrKTNrrYFWRK6grAFXUmZmVlpOUmZmVlpOUmZmVlpOUmZmVlqlSVKSzpMUkiYVHYuZmZVDKZKUpH2ANwOPFB2LmZmVRymSFHAZ8FEgig7EzMzKo/AkJekU4NGIuLef+eZLWiFpxYYNG4YpOjMzK9KwXMwr6RbgFTUmLQQuJDX1NRQRVwFXAfT09LjiMjPrAsOSpCLi+FrjJb0O2Be4VxLAFOAuSUdGxOPDEZuZmZVXobdFiohfAC+rDEtaA/RExFOFBWXWbXyDVyuxws9JmZmZ1VOqG8xGxPSiYzDrGv7RQesArqTMzKy0SlVJmdkw8o8OWgdwJWVmZqXlSsqs27mCshJzJWVmZqXlJGVmZqXlJGVmZqXlJGVmZqXlJGVmZqXlJGVmZqXlJGVmZqXlJGVmZqXlJGVmZqWliM77kVtJG4C12eAkoOy/P+UYW6MTYoTOiNMxtkYnxAg74pwWEZOLDmYgOjJJ5UlaERE9RcfRiGNsjU6IETojTsfYGp0QI3ROnLW4uc/MzErLScrMzEprJCSpq4oOoAmOsTU6IUbojDgdY2t0QozQOXHupOPPSZmZ2XDk0pgAAAOCSURBVMg1EiopMzMboZykzMystEZUkpJ0nqSQNKnoWKpJ+pSk+yTdI+lmSXsVHVM1SV+Q9EAW53ck7Vl0TNUkvUPSLyW9KKlUXWolzZb0oKRfSfpY0fHUIukaSU9KWlV0LPVI2kfSrZLuzz7rc4uOqZqksZJ+LuneLMaLio6pHkmjJd0t6caiYxmMEZOkJO0DvBl4pOhY6vhCRBwaEYcBNwIfLzqgGpYCr42IQ4GHgAUFx1PLKuBtwG1FB5InaTTw98AJwMHAqZIOLjaqmq4FZhcdRD+2AudFxMHA0cAHS7gu/wt4Q0S8HjgMmC3p6IJjqudcYHXRQQzWiElSwGXAR4FS9gSJiN/lBnejhHFGxM0RsTUbvAOYUmQ8tUTE6oh4sOg4ajgS+FVE/DoingeuB04pOKadRMRtwNNFx9FIRDwWEXdl/z9L2sHuXWxUfUWyKRsckz1K952WNAU4CfiHomMZrBGRpCSdAjwaEfcWHUsjki6WtA6YRzkrqbyzgJuKDqKD7A2syw2vp2Q71k4kaTowA/i3YiPZWdaMdg/wJLA0IkoXI3A56eD9xaIDGaxdig6gWZJuAV5RY9JC4EJSU1+hGsUYEd+LiIXAQkkLgLOBTwxrgPQfYzbPQlKTy5LhjK2imRht5JO0O/BPwIerWiJKISK2AYdl526/I+m1EVGac32S5gBPRsRKSbOKjmewOiZJRcTxtcZLeh2wL3CvJEhNVHdJOjIiHh/GEOvGWMMS4IcUkKT6i1HSGcAc4I1R0EV0A1iPZfIosE9ueEo2zgZB0hhSgloSEd8uOp5GIuIZSbeSzvWVJkkBxwInSzoRGAu8VNLiiDit4LgGpOOb+yLiFxHxsoiYHhHTSc0shw93guqPpANzg6cADxQVSz2SZpOaBk6OiM1Fx9Nh7gQOlLSvpF2BucD3C46pIykdbV4NrI6IS4uOpxZJkyu9XyWNA95Eyb7TEbEgIqZk+8W5wI87LUHBCEhSHeSzklZJuo/UNFm6brXAV4AJwNKsq/wVRQdUTdJbJa0HjgF+IOlHRccEkHU4ORv4EelE/zcj4pfFRrUzSb3Az4BXS1ov6T1Fx1TDscDpwBuy7fCerBook1cCt2bf5ztJ56Q6sot32fm2SGZmVlqupMzMrLScpMzMrLScpMzMrLScpMzMrLScpMzMrLScpMzMrLScpMzMrLT+P1puk1ig51WZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "pca = PCA(n_components=2).fit(selected_features_train)\n",
    "pca_2d = pca.transform(selected_features_train)\n",
    "import pylab as pl\n",
    "for i in range(0, pca_2d.shape[0]):\n",
    "    if y_train[i] == -1:\n",
    "        c1 = pl.scatter(pca_2d[i,0],pca_2d[i,1],c='r',    marker='+')\n",
    "    elif y_train[i] == 1:\n",
    "        c2 = pl.scatter(pca_2d[i,0],pca_2d[i,1],c='b',    marker='o')\n",
    "\n",
    "pl.legend([c1, c2], ['AD', 'MCI'])\n",
    "pl.title('Linear SVM classifier with Feature selection evaluated with 5 fold cv')\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "____________________________________________________________________________________"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from helpers.training import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "ranks,accs,weights=training(kf5,10,reliefF,X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([127, 356, 418, 332, 457,  99, 484, 388, 309, 421])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "pool_FS=[reliefF,lap_score,ll_l21,ls_l21,UDFS,fisher_score,chi_square,gini_index,SPEC]#,BorutaPy]#,MCFS]#trace_ratio,"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using 9 FS methods and  3 Cross-validation (5-fold, 10-fold, LOO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from helpers.training import *\n",
    "from helpers.fs_methods import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe_ranking_5fold,dataframe_accuracies_5fold,dataframe_weights_5fold=fs_training(kf5,X,y,num_fea)\n",
    "dataframe_ranking_10fold,dataframe_accuracies_10fold,dataframe_weights_10fold=fs_training(kf10,X,y,num_fea)\n",
    "dataframe_ranking_loo,dataframe_accuracies_loo,dataframe_weights_loo=fs_training(LOO,X,y,num_fea)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Overlapping matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from helpers.similarities import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe_ranking_5fold['overlapping']=dataframe_ranking_5fold.apply(\n",
    "    lambda row:FS_to_FS_similarity(row),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe_ranking_10fold['overlapping']=dataframe_ranking_10fold.apply(\n",
    "    lambda row:FS_to_FS_similarity(row),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataframe_ranking_loo['overlapping']=dataframe_ranking_loo.apply(\n",
    "    lambda row:FS_to_FS_similarity(row),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def check_symmetric(a, rtol=1e-05, atol=1e-08):\n",
    "    return np.allclose(a, a.T, rtol=rtol, atol=atol)\n",
    "check_symmetric(dataframe_ranking_5fold.overlapping[10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5-fold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'num_fea=100')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.suptitle('Overlapping matrices', fontsize=16)\n",
    "\n",
    "plt.subplot(2, 5, 1)\n",
    "plt.imshow(dataframe_ranking_5fold.overlapping[10],cmap=plt.cm.prism_r)\n",
    "plt.title('num_fea=10')\n",
    "plt.subplot(2, 5, 2)\n",
    "plt.imshow(dataframe_ranking_5fold.overlapping[20],cmap=plt.cm.prism_r)\n",
    "plt.title('num_fea=20')\n",
    "plt.subplot(2, 5, 3)\n",
    "plt.imshow(dataframe_ranking_5fold.overlapping[30],cmap=plt.cm.prism_r)\n",
    "plt.title('num_fea=30')\n",
    "plt.subplot(2, 5, 4)\n",
    "plt.imshow(dataframe_ranking_5fold.overlapping[40],cmap=plt.cm.prism_r)\n",
    "plt.title('num_fea=40')\n",
    "plt.subplot(2, 5, 5)\n",
    "plt.imshow(dataframe_ranking_5fold.overlapping[50],cmap=plt.cm.prism_r)\n",
    "plt.title('num_fea=50')\n",
    "plt.subplot(2, 5, 6)\n",
    "plt.imshow(dataframe_ranking_5fold.overlapping[60],cmap=plt.cm.prism_r)\n",
    "plt.title('num_fea=60')\n",
    "plt.subplot(2, 5, 7)\n",
    "plt.imshow(dataframe_ranking_5fold.overlapping[70],cmap=plt.cm.prism_r)\n",
    "plt.title('num_fea=70')\n",
    "plt.subplot(2, 5, 8)\n",
    "plt.imshow(dataframe_ranking_5fold.overlapping[80],cmap=plt.cm.prism_r)\n",
    "plt.title('num_fea=80')\n",
    "plt.subplot(2, 5, 9)\n",
    "plt.imshow(dataframe_ranking_5fold.overlapping[90],cmap=plt.cm.prism_r)\n",
    "plt.title('num_fea=90')\n",
    "plt.subplot(2, 5, 10)\n",
    "plt.imshow(dataframe_ranking_5fold.overlapping[100],cmap=plt.cm.prism_r)\n",
    "plt.title('num_fea=100')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 10-fold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'num_fea=100')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.suptitle('Overlapping matrices', fontsize=16)\n",
    "plt.subplot(2, 5, 1)\n",
    "plt.imshow(dataframe_ranking_10fold.overlapping[10],cmap=plt.cm.prism_r)\n",
    "plt.title('num_fea=10')\n",
    "plt.subplot(2, 5, 2)\n",
    "plt.imshow(dataframe_ranking_10fold.overlapping[20],cmap=plt.cm.prism_r)\n",
    "plt.title('num_fea=20')\n",
    "plt.subplot(2, 5, 3)\n",
    "plt.imshow(dataframe_ranking_10fold.overlapping[30],cmap=plt.cm.prism_r)\n",
    "plt.title('num_fea=30')\n",
    "plt.subplot(2, 5, 4)\n",
    "plt.imshow(dataframe_ranking_10fold.overlapping[40],cmap=plt.cm.prism_r)\n",
    "plt.title('num_fea=40')\n",
    "plt.subplot(2, 5, 5)\n",
    "plt.imshow(dataframe_ranking_10fold.overlapping[50],cmap=plt.cm.prism_r)\n",
    "plt.title('num_fea=50')\n",
    "plt.subplot(2, 5, 6)\n",
    "plt.imshow(dataframe_ranking_10fold.overlapping[60],cmap=plt.cm.prism_r)\n",
    "plt.title('num_fea=60')\n",
    "plt.subplot(2, 5, 7)\n",
    "plt.imshow(dataframe_ranking_10fold.overlapping[70],cmap=plt.cm.prism_r)\n",
    "plt.title('num_fea=70')\n",
    "plt.subplot(2, 5, 8)\n",
    "plt.imshow(dataframe_ranking_10fold.overlapping[80],cmap=plt.cm.prism_r)\n",
    "plt.title('num_fea=80')\n",
    "plt.subplot(2, 5, 9)\n",
    "plt.imshow(dataframe_ranking_10fold.overlapping[90],cmap=plt.cm.prism_r)\n",
    "plt.title('num_fea=90')\n",
    "plt.subplot(2, 5, 10)\n",
    "plt.imshow(dataframe_ranking_10fold.overlapping[100],cmap=plt.cm.prism_r)\n",
    "plt.title('num_fea=100')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### LOO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'num_fea=100')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.suptitle('Overlapping matrices', fontsize=16)\n",
    "plt.subplot(2, 5, 1)\n",
    "plt.imshow(dataframe_ranking_loo.overlapping[10],cmap=plt.cm.prism_r)\n",
    "plt.title('num_fea=10')\n",
    "plt.subplot(2, 5, 2)\n",
    "plt.imshow(dataframe_ranking_loo.overlapping[20],cmap=plt.cm.prism_r)\n",
    "plt.title('num_fea=20')\n",
    "plt.subplot(2, 5, 3)\n",
    "plt.imshow(dataframe_ranking_loo.overlapping[30],cmap=plt.cm.prism_r)\n",
    "plt.title('num_fea=30')\n",
    "plt.subplot(2, 5, 4)\n",
    "plt.imshow(dataframe_ranking_loo.overlapping[40],cmap=plt.cm.prism_r)\n",
    "plt.title('num_fea=40')\n",
    "plt.subplot(2, 5, 5)\n",
    "plt.imshow(dataframe_ranking_loo.overlapping[50],cmap=plt.cm.prism_r)\n",
    "plt.title('num_fea=50')\n",
    "plt.subplot(2, 5, 6)\n",
    "plt.imshow(dataframe_ranking_loo.overlapping[60],cmap=plt.cm.prism_r)\n",
    "plt.title('num_fea=60')\n",
    "plt.subplot(2, 5, 7)\n",
    "plt.imshow(dataframe_ranking_loo.overlapping[70],cmap=plt.cm.prism_r)\n",
    "plt.title('num_fea=70')\n",
    "plt.subplot(2, 5, 8)\n",
    "plt.imshow(dataframe_ranking_loo.overlapping[80],cmap=plt.cm.prism_r)\n",
    "plt.title('num_fea=80')\n",
    "plt.subplot(2, 5, 9)\n",
    "plt.imshow(dataframe_ranking_loo.overlapping[90],cmap=plt.cm.prism_r)\n",
    "plt.title('num_fea=90')\n",
    "plt.subplot(2, 5, 10)\n",
    "plt.imshow(dataframe_ranking_loo.overlapping[100],cmap=plt.cm.prism_r)\n",
    "plt.title('num_fea=100')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Average similarity matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "average_similarity_5fold=dataframe_ranking_5fold.overlapping.mean()\n",
    "average_similarity_10fold=dataframe_ranking_10fold.overlapping.mean()\n",
    "average_similarity_loo=dataframe_ranking_loo.overlapping.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Average similarity case 5 fold CV')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sns.heatmap(dataframe_ranking_5fold.overlapping.mean())\n",
    "plt.imshow(average_similarity_5fold,cmap=plt.cm.prism_r)\n",
    "plt.title('Average similarity case 5 fold CV')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Average similarity case 10 fold CV')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(average_similarity_10fold,cmap=plt.cm.prism_r)\n",
    "plt.title('Average similarity case 10 fold CV')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Average similarity case LOO CV')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(average_similarity_loo,cmap=plt.cm.prism_r)\n",
    "plt.title('Average similarity case LOO CV')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accuracy similarity matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_5fold_acc=dataframe_accuracies_5fold.copy()\n",
    "df_10fold_acc=dataframe_accuracies_10fold.copy()\n",
    "df_loo_acc=dataframe_accuracies_loo.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_5fold_acc.loc['mean'] =df_5fold_acc.mean()\n",
    "df_10fold_acc.loc['mean'] =df_10fold_acc.mean()\n",
    "df_loo_acc.loc['mean'] =df_loo_acc.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc_sim_matrix_5fold=matrix_acc(list(df_5fold_acc.loc['mean']))\n",
    "acc_sim_matrix_10fold=matrix_acc(list(df_10fold_acc.loc['mean']))\n",
    "acc_sim_matrix_loo=matrix_acc(list(df_loo_acc.loc['mean']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Average accuracy similarity matrix case 5-fold CV')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MrBgOPDMrhgPPzIrRysCTtKukayQtkXSrpFOarsnMuq+Vv3gMrANOjYhFkqYCCyV9LyKWNF2YmXVXK1t4EXFPRCzKjx8FlgLTm63KzLqulYFXJWkmMAu4oW/4+n5pmyjMzDqn1YEnaWvgcuB9EfFIddwz+qVtpjwz65jWBp6kzUhhNy8ivtp0PWbWfa0MPEkCzgGWRsTnmq7HzCaGVgYecDAwFzhM0uL87zVNF2Vm3dbKr6VExHVAvT/fambFaWsLz8xso3PgmVkxHHhmVgwHnpkVw4FnZsVw4JlZMVr5tZS2Ofbpeud/6b/UO3+g/o6yf7/m+X+n3tnrdfV35R5X1vxNq5pnPxH6WncLz8yK4cAzs2I48MysGA48MyuGA8/MiuHAM7NiOPDMrBgOPDMrRisDT9KWkn4s6ZbcL+3fNV2TmXVfW//S4lfAYRHxWO7b4jpJV0XE9U0XZmbd1crAi4gAHstPN8v/ormKzGwiaOUpLYCkSZIWA/cB34sI90trZhuktYEXEU9FxP7ADOBASS/uG+9+ac1sIK0NvJ6IeAi4Bjiq6VrMrNtaGXiSdpQ0LT9+LnAkcFuzVZlZ17XypgWwM/AlSZNIoXxJRHyj4ZrMrONaGXgR8RNgVtN1mNnE0spTWjOzOjjwzKwYDjwzK4YDz8yK4cAzs2I48MysGEp/p99ts6Wov1fRjut4v7HWPMHCiJjddB0bwi08MyuGA8/MiuHAM7NiOPDMrBgOPDMrhgPPzIrhwDOzYjjwzKwYrQ683JHPzZL8459mtsFaHXjAKcDSposws4mhtYEnaQbwWuDspmsxs4mhtYEHnAGcBjw91Ej3S2tmg2pl4En6A+C+iFg43DTul9bMBtXKwAMOBl4v6S7gy8Bhki5stiQz67pWBl5EfCgiZkTETOCtwNUR8faGyzKzjmtl4JmZ1aGV/dJWRcQCYEHDZZjZBOAWnpkVw4FnZsVw4JlZMRx4ZlYMB56ZFcOBZ2bFaP3XUlphp5rnf2/N8wf3GzuKKd9/ae3LePyIH9W+DBuZW3hmVgwHnpkVw4FnZsVw4JlZMRx4ZlYMB56ZFcOBZ2bFcOCZWTFa+8Xj/PPujwJPAesiYnazFZlZ17U28LJXRcTaposws4nBp7RmVow2B14A35W0UNIf9490v7RmNqg2n9K+PCJWSfoN4HuSbouIa3sjI+Is4CyA2VI0VaSZdUdrW3gRsSr/fx8wHziw2YrMrOtaGXiSpkia2nsMvBr4abNVmVnXtfWUdidgviRINV4UEd9utiQz67pWBl5E3Ans13QdZjaxtPKU1sysDg48MyuGA8/MiuHAM7NiOPDMrBgOPDMrRiu/ltI6m6Lf2JrpdTfVOv+4st5f76q739hN0WesLnhnvQu48sR653/JIfXOfxNwC8/MiuHAM7NiOPDMrBgOPDMrhgPPzIrhwDOzYjjwzKwYDjwzK0ZrA0/SNEmXSbpN0lJJ9X7z1MwmvDb/pcUXgG9HxJskbQ5s1XRBZtZtrQw8SdsAhwInAETEE8ATTdZkZt3X1lPa3YE1wBcl3Szp7NyZz6+5X1ozG1RbA28ycABwZkTMAh4HTq9OEBFnRcTsiJi9YxMVmlnntDXwVgIrI+KG/PwyUgCamY1bKwMvIlYDKyTtmQcdDixpsCQzmwBaedMiew8wL9+hvROo+ce+zGyia23gRcRioN5flTSzorTylNbMrA4OPDMrhgPPzIrhwDOzYjjwzKwYDjwzK4YDz8yKoYhouoYNNluKOruZvoBFNc4d/p1Ztc4f4FpU+zK6rPZOsoGYe27ty6iTYGFEdPq7sW7hmVkxHHhmVgwHnpkVw4FnZsVw4JlZMRx4ZlYMB56ZFaOVgSdpT0mLK/8ekfS+pusys25r5Q+ARsTtwP4AkiYBq4D5jRZlZp3XyhZen8OBZRGxvOlCzKzbuhB4bwUubroIM+u+Vgde7sDn9cClQ4xzR9xmNpBWBx4wB1gUEff2j3BH3GY2qLYH3nH4dNbMNpLWBp6kKcCRwFebrsXMJoZWfi0FICIeB7Zvug4zmzha28IzM9vYHHhmVgwHnpkVw4FnZsVw4JlZMRx4ZlYMB56ZFaO138Nrk5Nq7jf2s7EJ+ox1t7Qju/LETbCQbvdLOxG4hWdmxXDgmVkxHHhmVgwHnpkVw4FnZsVw4JlZMRx4ZlYMB56ZFaO1gSfp/ZJulfRTSRdL2rLpmsys21oZeJKmA+8FZkfEi4FJpO4azczGrZWBl00GnitpMrAV8IuG6zGzjmtl4EXEKuAzwN3APcDDEfHd6jTul9bMBtXKwJO0LXA0sDuwCzBF0tur07hfWjMbVCsDDzgC+J+IWBMRT5K6anxZwzWZWce1NfDuBn5P0laSBBwOLG24JjPruFYGXkTcAFwGLAL+m1TnWY0WZWad19ofAI2IjwAfaboOM5s4WtnCMzOrgwPPzIrhwDOzYjjwzKwYDjwzK4YDz8yKoYhouoYNJmkNsHzAl+0ArK2hnE3F9Tev6+swaP27RUSn/5JzQgTeeEi6KSJmN13HeLn+5nV9Hbpe/3j4lNbMiuHAM7NilBx4Xf/bXNffvK6vQ9frH1ix1/DMrDwlt/DMrDAOPDMrRnGBJ+koSbdLukPS6U3XMwhJu0q6RtKS3IXlKU3XNF6SJkm6WdI3mq5lUJKmSbpM0m2Slkp6adM1DaLkLlCLCjxJk4B/BuYAewPHSdq72aoGsg44NSL2Bn4P+IuO1V91Ct39FesvAN+OiL2A/ejQepTeBWpRgQccCNwREXdGxBPAl0mdBXVCRNwTEYvy40dJO9r0ZqsanKQZwGuBs5uuZVCStgEOBc4BiIgnIuKhZqsaWLFdoJYWeNOBFZXnK+lgYABImgnMAm5otpJxOQM4DXi66ULGYXdgDfDFfEp+tqQpTRc1VmPpAnUiKy3wJgRJWwOXA++LiEearmcQkv4AuC8iFjZdyzhNBg4AzoyIWcDjQGeuBY+lC9SJrLTAWwXsWnk+Iw/rDEmbkcJuXkR8tel6xuFg4PWS7iJdUjhM0oXNljSQlcDK3NEUpM6mDmiwnkEV3QVqaYF3I/ACSbtL2px0sfaKhmsas9xl5TnA0oj4XNP1jEdEfCgiZkTETNL7f3VEdKaFERGrgRWS9syDDgeWNFjSoIruArW1vZbVISLWSToZ+A7p7tS5EXFrw2UN4mBgLvDfkhbnYX8dEd9qsKYSvQeYlw+adwInNlzPmEXEDZJ6XaCuA26moD8x85+WmVkxSjulNbOCOfDMrBgOPDMrhgPPzIrhwDOzYjjwzKwYDjwzK8b/A2OAYLjVxU2NAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(acc_sim_matrix_5fold,cmap=plt.cm.prism_r)\n",
    "plt.title('Average accuracy similarity matrix case 5-fold CV')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Average accuracy similarity matrix case 10-fold CV')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(acc_sim_matrix_10fold,cmap=plt.cm.prism_r)\n",
    "plt.title('Average accuracy similarity matrix case 10-fold CV')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Average accuracy similarity matrix case loo CV')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(acc_sim_matrix_loo,cmap=plt.cm.prism_r)\n",
    "plt.title('Average accuracy similarity matrix case loo CV')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Final similarity without stability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Final similarity matrix without stability: 5-fold cv')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Final_sim_5fold=np.multiply(average_similarity_5fold,acc_sim_matrix_5fold)\n",
    "plt.imshow(Final_sim_5fold,cmap=plt.cm.prism_r)\n",
    "plt.title('Final similarity matrix without stability: 5-fold cv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Final similarity matrix without stability: 10-fold cv')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Final_sim_10fold=np.multiply(average_similarity_10fold,acc_sim_matrix_10fold)\n",
    "plt.imshow(Final_sim_10fold,cmap=plt.cm.prism_r)\n",
    "plt.title('Final similarity matrix without stability: 10-fold cv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Final similarity matrix without stability: LOO cv')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Final_sim_loo=np.multiply(average_similarity_loo,acc_sim_matrix_loo)\n",
    "plt.imshow(Final_sim_loo,cmap=plt.cm.prism_r)\n",
    "plt.title('Final similarity matrix without stability: LOO cv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Binary overlapping for FS reliefF and k =10 (5-fold,10fold and LOO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from helpers.reproducibility import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def binary_intersection(a,b,k):\n",
    "    ''' A function that return the percentage of common elements to both a and b'''\n",
    "    sum_=0\n",
    "    for x in range(a.shape[0]):\n",
    "        for y in range(a.shape[1]):\n",
    "            sum_+=np.sum((a[x,y]==b[x,y]))\n",
    "    #percentage=(sum_*100)/k\n",
    "    percentage=(sum_*100)/b.size\n",
    "    return(percentage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def binary_matrice(FS_k,k):\n",
    "    n=len(FS_k)\n",
    "    m=np.ones((n,n))\n",
    "    for i in range(n):\n",
    "        for j in range(i+1,n):\n",
    "            m[i,j]=binary_intersection(FS_k[i],FS_k[j],k)\n",
    "            m[j,i]=m[i,j]\n",
    "   # for i in range(n):\n",
    "    #    m[i,i]=intersection(FS_k[i],FS_k[i])/100\n",
    "    return(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "FS_k=[dataframe_ranking_5fold.overlapping[10],dataframe_ranking_10fold.overlapping[10],\n",
    "      dataframe_ranking_loo.overlapping[10]]\n",
    "k=10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels=['5fold','10fold','LOO']\n",
    "dataframe=pd.DataFrame(binary_matrice(FS_k,k),columns=labels,index=labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'The overlap in % between top 10 features discovered using a pair of CV methods')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sns.heatmap(dataframe)\n",
    "plt.imshow(dataframe)\n",
    "plt.title('The overlap in % between top 10 features discovered using a pair of CV methods')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<module 'skfeature.function.similarity_based.reliefF' from '/home/raoua/anaconda3/lib/python3.6/site-packages/skfeature/function/similarity_based/reliefF.py'>,\n",
       " <module 'skfeature.function.similarity_based.lap_score' from '/home/raoua/anaconda3/lib/python3.6/site-packages/skfeature/function/similarity_based/lap_score.py'>,\n",
       " <module 'skfeature.function.sparse_learning_based.ll_l21' from '/home/raoua/anaconda3/lib/python3.6/site-packages/skfeature/function/sparse_learning_based/ll_l21.py'>,\n",
       " <module 'skfeature.function.sparse_learning_based.ls_l21' from '/home/raoua/anaconda3/lib/python3.6/site-packages/skfeature/function/sparse_learning_based/ls_l21.py'>,\n",
       " <module 'skfeature.function.sparse_learning_based.UDFS' from '/home/raoua/anaconda3/lib/python3.6/site-packages/skfeature/function/sparse_learning_based/UDFS.py'>,\n",
       " <module 'skfeature.function.similarity_based.fisher_score' from '/home/raoua/anaconda3/lib/python3.6/site-packages/skfeature/function/similarity_based/fisher_score.py'>,\n",
       " <module 'skfeature.function.statistical_based.chi_square' from '/home/raoua/anaconda3/lib/python3.6/site-packages/skfeature/function/statistical_based/chi_square.py'>,\n",
       " <module 'skfeature.function.statistical_based.gini_index' from '/home/raoua/anaconda3/lib/python3.6/site-packages/skfeature/function/statistical_based/gini_index.py'>,\n",
       " <module 'skfeature.function.similarity_based.SPEC' from '/home/raoua/anaconda3/lib/python3.6/site-packages/skfeature/function/similarity_based/SPEC.py'>]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pool_FS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Without stability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Identify the most reproducible feature selection method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Centrality measure \n",
    "import networkx as nx\n",
    "G_5fold=nx.from_numpy_matrix(Final_sim_5fold)\n",
    "#centrality_5fold=nx.degree_centrality(G_5fold)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "centrality_5fold"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "import networkx as nx\n",
    "normalized_G=nx.from_numpy_matrix(Final_sim_5fold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "G_10fold=nx.from_numpy_matrix(Final_sim_10fold)\n",
    "#centrality_10fold=nx.degree_centrality(G_10fold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "G_loo=nx.from_numpy_matrix(Final_sim_loo)\n",
    "#centrality_loo=nx.degree_centrality(G_loo)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "centrality_loo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate betweenness and closeness centrality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "bc_5fold=nx.betweenness_centrality(G_5fold)\n",
    "cc_5fold=nx.closeness_centrality(G_5fold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "bc_10fold=nx.betweenness_centrality(G_10fold)\n",
    "cc_10fold=nx.closeness_centrality(G_10fold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "bc_loo=nx.betweenness_centrality(G_loo)\n",
    "cc_loo=nx.closeness_centrality(G_loo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0.023809523809523808,\n",
       " 1: 0.013095238095238094,\n",
       " 2: 0.04285714285714285,\n",
       " 3: 0.1125,\n",
       " 4: 0.04583333333333332,\n",
       " 5: 0.007142857142857143,\n",
       " 6: 0.05595238095238095,\n",
       " 7: 0.026785714285714284,\n",
       " 8: 0.06488095238095237}"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bc_5fold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0.7272727272727273,\n",
       " 1: 0.6666666666666666,\n",
       " 2: 0.8,\n",
       " 3: 0.8888888888888888,\n",
       " 4: 0.8,\n",
       " 5: 0.6666666666666666,\n",
       " 6: 0.8,\n",
       " 7: 0.7272727272727273,\n",
       " 8: 0.8888888888888888}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_5fold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Betweeness centrality 5-fold')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(bc_5fold.values())\n",
    "plt.title('Betweeness centrality 5-fold')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'closeness centrality 5-fold')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEICAYAAAC3Y/QeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAUpUlEQVR4nO3de7RedX3n8fcHwsXKVXLGYiAJVrSCM4hGvNWR5Y2LVKYVp6BFUJxUpla7qtMB7aIOq+3gmo52LExZqToI06p4GVequFjMgFWnggTKHdEICImoIdzFS9Hv/LF3Og+Hc87zJOc5eZL83q+19sp+9v799v7uX57zOfvs/VxSVUiS2rLTpAuQJG19hr8kNcjwl6QGGf6S1CDDX5IaZPhLUoMM/0YlOTXJ1yZdR4uSXJDkT/r5lyW5bYK1nJ7kB0keSbLfkLZfTvK2WdYtT1JJFi1MpRo3w1/aDEnuTPKqcW2vqr5aVc8a1/b7AP5RH+aPJPnIHG13AT4IvKaq9qiqjVu6X21//C0tjVGSRVX12ITLOKyq1o7Q7qnA7sDNC1yPtkGe+e/gkhyY5HNJNiTZmOTcWdq9JMnVSR7s/33JwLpTk9ye5OEkdyR508C6tya5Ncn9SS5NsmxgXSV5e5JvJ3kgyXlJMqxvOh9K8sMkDyW5Mclz+nXHJrmlr2V9kvfMcez/rt/+w32f5/XLn5bks/2Y3JHknQN93p/k4iQX9v1uTrKiX3cRsBT4u/6s+g8HLnecluQu4PK+7aeTfL8fz68kOXSWGo9Msm6O7X8xye9N63NDkt+Y7bhHkeSZwKbLTQ8k2VT3rM+Daf13TvLnSe5Ncjvw2vnUowmoKqcddAJ2Bq4HPgQ8me4s79f6dacCX+vnnwLcD5xM99fgSf3j/fp+DwHP6tvuDxzazx8PrAWe3ff7I+AfBvZfwBeAfehCbQNw9LC+wFHANX2/9G3279fdA7ysn98XeN4sx/4GYD3wgn4bzwCW0Z3wXAOcBewKPB24HTiq7/d+4CfAsf34/WfgyoHt3gm8auDx8v44L+zH6kn98rcCewK7AX8BXDfQ5wLgT/r5I4F1c2z/3wJXDTw+DNgI7DrLcRfwPeD7wOeA5XM8PzbVvmjY86Bf/2Xgbf3824FvAgf2/a4Y3JbTtj9NvACnBfzPhRf3gfuEH0geH/4nA9+Ytv7rfZsnAw8Ar98UbANtvgScNvB4J+BRYFn/uOh/2fSPLwbOGNYXeAXwLeBFwE7T9nkX8DvAXkOO/VLgXTMsfyFw17RlZwL/o59/P/C/B9YdAvx44PH0cN4UoE+fo5Z9+jZ79483J/x37wP44P7xnwP/fY59/Wu6X2r7AOcCN80WyDOE/6zPg35+MPwvB94+0O41hv/2NXnZZ8d2IPDdGn4N+mnAd6ct+y6wpKp+BPwW3ZnePf1liF/t2ywD/lt/SecB4D66s+wlA9v5/sD8o8Aew/pW1eV0wXUe8MMkq5Ls1fd7Pd1Z+XeT/H2SF89x7N+ZYfky4Gmb9tvv+710179nq3n3EV7Fcvemmf6SyDlJvpPkIbpAB1g8ZBtPUFU/AT4F/HaSnejOxi+ao/1XqupnVfUA8C7gIODZSZYO3AR+ZJbusz4PZml797R22o4Y/ju2u4GlIwTX9+hCcdBSussmVNWlVfVquks+3wT+emD7v1NV+wxMT6qqfxixtln7VtWHq+r5dGfezwT+Q7/86qo6HvgXwOfp/pqYbfu/MsvyO6btd8+qOnaEmqE7ux22/I10l7VeBexNd4YN3S+3Ldn+x4E3Aa8EHq2qr49U6f/fXqrqrupe0bNHVe0xS9s5nwfT3EP3C3awnbYjhv+O7Rt0P6TnJHlykt2TvHSGdpcAz0zyxiSLkvwWXeh+IclTkxyf5MnAT4FHgF/0/c4Hztx0MzPJ3kneMGJts/ZN8oIkL0z3UsQf0V2D/0WSXZO8KcneVfVPdPcifjHL9j8CvCfJ8/sbyM/obyh/A3g4yX9M8qT+LP05SV4wYt0/oLtPMJc96cZqI/BLwJ+NuO0Zt9+H/S+A/8ocZ/1JDk3y3P6Y9ujbrwduHXHfsz4PZmh7MfDOJAck2Rc4Y8R9aBth+O/AqurnwK/T3ey8C1hHdwlneruNwHHAu+kC6w+B46rqXrrnyB/QnRXeB7wcOL3v97+ADwCf7C9v3AQcM2Jtc/Xdi+6vi/vpLidsBP5Lv+5k4M6+z9vpzohn2v6ngT8F/hZ4mO6vhKf0Y3Ic8FzgDuBeul8Ue49SN90N4D/qLxnN9kqjC/u61wO3AFeOuO25tn8h8C+B/zlH36fSXSJ6iO4m9nK6/8d/GmXHQ54H0/013X2V64Fr6W4uazuSKr/MRdrWJXkzsLKqfm3StWjH4Jm/tI1L8kvAvwdWTboW7TgMf2kbluQoupfr/oDuEpY0Fl72kaQGeeYvSQ2a2Ae7LV68uJYvXz6p3UvSdumaa665t6qm5rudiYX/8uXLWbNmzaR2L0nbpSRjeTe1l30kqUGGvyQ1yPCXpAYZ/pLUIMNfkhpk+EtSg4aGf/8xwN9Icn3/fab/aYY2uyX5VJK1Sa5KsnwhipUkjccoZ/4/BV5RVYfRfQzu0UleNK3NacD9VfUMuu+L/cB4y5QkjdPQ8K/Opq9926Wfpn8g0PF03zYE8BnglUlG+dYiSdIEjPQO3yQ7A9fQfSnIeVV11bQmS+i/z7OqHkvyILAf3RdlDG5nJbASYOlSv/VNmm75GV+cyH7vPOe1E9mvJmekG75V9fOqei5wAHBEkudsyc6qalVVraiqFVNT8/5oCknSFtqsV/tU1QPAFcDR01atp/8y5/7Lwvem+xo4SdI2aJRX+0wl2aeffxLwauCb05qtBk7p508ALi+/KECStlmjXPPfH/h4f91/J+DiqvpCkrOBNVW1GvgocFGStXRf8n3iglUsSZq3oeFfVTcAh8+w/KyB+Z8AbxhvaZKkheI7fCWpQYa/JDXI8JekBhn+ktQgw1+SGmT4S1KDDH9JapDhL0kNMvwlqUGGvyQ1yPCXpAYZ/pLUIMNfkhpk+EtSgwx/SWqQ4S9JDTL8JalBhr8kNcjwl6QGGf6S1CDDX5IaZPhLUoMMf0lqkOEvSQ0y/CWpQUPDP8mBSa5IckuSm5O8a4Y2RyZ5MMl1/XTWwpQrSRqHRSO0eQx4d1Vdm2RP4Jokl1XVLdPafbWqjht/iZKkcRt65l9V91TVtf38w8CtwJKFLkyStHA265p/kuXA4cBVM6x+cZLrk3wpyaGz9F+ZZE2SNRs2bNjsYiVJ4zFy+CfZA/gs8PtV9dC01dcCy6rqMOAvgc/PtI2qWlVVK6pqxdTU1JbWLEmap5HCP8kudMH/N1X1uenrq+qhqnqkn78E2CXJ4rFWKkkam1Fe7RPgo8CtVfXBWdr8ct+OJEf02904zkIlSeMzyqt9XgqcDNyY5Lp+2XuBpQBVdT5wAnB6kseAHwMnVlUtQL2SpDEYGv5V9TUgQ9qcC5w7rqIkSQvLd/hKUoMMf0lqkOEvSQ0y/CWpQYa/JDXI8JekBhn+ktQgw1+SGmT4S1KDDH9JapDhL0kNMvwlqUGGvyQ1yPCXpAYZ/pLUIMNfkhpk+EtSgwx/SWqQ4S9JDTL8JalBhr8kNcjwl6QGGf6S1CDDX5IaZPhLUoMMf0lq0NDwT3JgkiuS3JLk5iTvmqFNknw4ydokNyR53sKUK0kah0UjtHkMeHdVXZtkT+CaJJdV1S0DbY4BDu6nFwJ/1f8rSdoGDT3zr6p7qurafv5h4FZgybRmxwMXVudKYJ8k+4+9WknSWIxy5v/PkiwHDgeumrZqCXD3wON1/bJ7pvVfCawEWLp06eZVOmD5GV/c4r7zdec5r53Ifls8Zmkhtf4zNfIN3yR7AJ8Ffr+qHtqSnVXVqqpaUVUrpqamtmQTkqQxGCn8k+xCF/x/U1Wfm6HJeuDAgccH9MskSdugUV7tE+CjwK1V9cFZmq0G3ty/6udFwINVdc8sbSVJEzbKNf+XAicDNya5rl/2XmApQFWdD1wCHAusBR4F3jL+UiVJ4zI0/Kvqa0CGtCngd8dVlCRpYfkOX0lqkOEvSQ0y/CWpQYa/JDXI8JekBhn+ktQgw1+SGmT4S1KDDH9JapDhL0kNMvwlqUGGvyQ1yPCXpAYZ/pLUIMNfkhpk+EtSgwx/SWqQ4S9JDTL8JalBhr8kNcjwl6QGGf6S1CDDX5IaZPhLUoMMf0lq0NDwT/KxJD9MctMs649M8mCS6/rprPGXKUkap0UjtLkAOBe4cI42X62q48ZSkSRpwQ0986+qrwD3bYVaJElbybiu+b84yfVJvpTk0NkaJVmZZE2SNRs2bBjTriVJm2sc4X8tsKyqDgP+Evj8bA2ralVVraiqFVNTU2PYtSRpS8w7/Kvqoap6pJ+/BNglyeJ5VyZJWjDzDv8kv5wk/fwR/TY3zne7kqSFM/TVPkk+ARwJLE6yDvhjYBeAqjofOAE4PcljwI+BE6uqFqxiSdK8DQ3/qjppyPpz6V4KKknaTvgOX0lqkOEvSQ0y/CWpQYa/JDXI8JekBhn+ktQgw1+SGmT4S1KDDH9JapDhL0kNMvwlqUGGvyQ1yPCXpAYZ/pLUIMNfkhpk+EtSgwx/SWqQ4S9JDTL8JalBhr8kNcjwl6QGGf6S1CDDX5IaZPhLUoMMf0lqkOEvSQ0aGv5JPpbkh0lummV9knw4ydokNyR53vjLlCSN0yhn/hcAR8+x/hjg4H5aCfzV/MuSJC2koeFfVV8B7pujyfHAhdW5Etgnyf7jKlCSNH7juOa/BLh74PG6ftkTJFmZZE2SNRs2bBjDriVJW2Kr3vCtqlVVtaKqVkxNTW3NXUuSBowj/NcDBw48PqBfJknaRo0j/FcDb+5f9fMi4MGqumcM25UkLZBFwxok+QRwJLA4yTrgj4FdAKrqfOAS4FhgLfAo8JaFKlaSNB5Dw7+qThqyvoDfHVtFkqQF5zt8JalBhr8kNcjwl6QGGf6S1CDDX5IaZPhLUoMMf0lqkOEvSQ0y/CWpQYa/JDXI8JekBhn+ktQgw1+SGmT4S1KDDH9JapDhL0kNMvwlqUGGvyQ1yPCXpAYZ/pLUIMNfkhpk+EtSgwx/SWqQ4S9JDTL8JalBI4V/kqOT3JZkbZIzZlh/apINSa7rp7eNv1RJ0rgsGtYgyc7AecCrgXXA1UlWV9Ut05p+qqresQA1SpLGbJQz/yOAtVV1e1X9DPgkcPzCliVJWkijhP8S4O6Bx+v6ZdO9PskNST6T5MCZNpRkZZI1SdZs2LBhC8qVJI3DuG74/h2wvKr+FXAZ8PGZGlXVqqpaUVUrpqamxrRrSdLmGiX81wODZ/IH9Mv+WVVtrKqf9g8/Ajx/POVJkhbCKOF/NXBwkoOS7AqcCKwebJBk/4GHrwNuHV+JkqRxG/pqn6p6LMk7gEuBnYGPVdXNSc4G1lTVauCdSV4HPAbcB5y6gDVLkuZpaPgDVNUlwCXTlp01MH8mcOZ4S5MkLRTf4StJDTL8JalBhr8kNcjwl6QGGf6S1CDDX5IaZPhLUoMMf0lqkOEvSQ0y/CWpQYa/JDXI8JekBhn+ktQgw1+SGmT4S1KDDH9JapDhL0kNMvwlqUGGvyQ1yPCXpAYZ/pLUIMNfkhpk+EtSgwx/SWqQ4S9JDTL8JalBI4V/kqOT3JZkbZIzZli/W5JP9euvSrJ83IVKksZnaPgn2Rk4DzgGOAQ4Kckh05qdBtxfVc8APgR8YNyFSpLGZ5Qz/yOAtVV1e1X9DPgkcPy0NscDH+/nPwO8MknGV6YkaZwWjdBmCXD3wON1wAtna1NVjyV5ENgPuHewUZKVwMr+4SNJbtuSoicp8/ubZjHTxmR7MM9jHma7HJMFNJHxWOD/4/na4Z4j8xzvZ42jhlHCf2yqahWwamvuc1uSZE1VrZh0HdsSx+TxHI8nckweL8macWxnlMs+64EDBx4f0C+bsU2SRcDewMZxFChJGr9Rwv9q4OAkByXZFTgRWD2tzWrglH7+BODyqqrxlSlJGqehl336a/jvAC4FdgY+VlU3JzkbWFNVq4GPAhclWQvcR/cLQk/U7CWvOTgmj+d4PJFj8nhjGY94gi5J7fEdvpLUIMNfkhpk+I/JCB+B8aEk1/XTt5I8MLDulCTf7qdTpvfdHs1zPH4+sG76iwu2WyOMydIkVyT5xyQ3JDl2YN2Zfb/bkhy1dStfGFs6HkmWJ/nxwHPk/K1f/cIYYUyWJfk//Xh8OckBA+s2L0eqymmeE92N8O8ATwd2Ba4HDpmj/e/R3TgHeApwe//vvv38vpM+pkmNR//4kUkfwyTGhO5G3un9/CHAnQPz1wO7AQf129l50sc0wfFYDtw06WOY0Jh8Gjiln38FcFE/v9k54pn/eIzyERiDTgI+0c8fBVxWVfdV1f3AZcDRC1rtwpvPeOyoRhmTAvbq5/cGvtfPHw98sqp+WlV3AGv77W3P5jMeO6pRxuQQ4PJ+/oqB9ZudI4b/eMz0ERhLZmqYZBnd2dum/8CR+25H5jMeALsnWZPkyiT/ZuHK3KpGGZP3A7+dZB1wCd1fRKP23d7MZzwADuovB/19kpctaKVbzyhjcj3wm/38bwB7JtlvxL6PY/hvfScCn6mqn0+6kG3ETOOxrLq3878R+IskvzKZ0ra6k4ALquoA4Fi69860/DM623jcAyytqsOBPwD+Nslec2xnR/Ie4OVJ/hF4Od2nK2xRlrT8xBqnUT4CY5MTefwljs3pu72Yz3hQVev7f28HvgwcPv4St7pRxuQ04GKAqvo6sDvdh5q1+hyZcTz6y18b++XX0F0nf+aCV7zwho5JVX2vqn6z/8X3vn7ZA6P0fYJJ3+TYESa6d0rfTnf5YtONmkNnaPerwJ30b64buFFzB91Nmn37+adM+pgmOB77Arv184uBbzPHzeLtZRplTIAvAaf288+mu8Yd4FAef8P3drb/G77zGY+pTcdPd3N0/fb+M7MZY7IY2Kmf/1Pg7H5+s3Nk4ge8o0x0f5Z+i+4s5H39srOB1w20eT9wzgx930p3E28t8JZJH8skxwN4CXBj/8S/ETht0seytcaE7mbe/+2P/TrgNQN939f3uw04ZtLHMsnxAF4P3Nwvuxb49Ukfy1YckxPoToi+BXyE/kSpX7dZOeLHO0hSg7zmL0kNMvwlqUGGvyQ1yPCXpAYZ/pLUIMNfkhpk+EtSg/4fdqpz7suenbIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(cc_5fold.values())\n",
    "plt.title('closeness centrality 5-fold')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0.08035714285714284,\n",
       " 1: 0.07738095238095238,\n",
       " 2: 0.05059523809523809,\n",
       " 3: 0.23809523809523808,\n",
       " 4: 0.0,\n",
       " 5: 0.07142857142857142,\n",
       " 6: 0.008928571428571428,\n",
       " 7: 0.011904761904761904,\n",
       " 8: 0.13988095238095236}"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bc_10fold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0.7272727272727273,\n",
       " 1: 0.6666666666666666,\n",
       " 2: 0.6666666666666666,\n",
       " 3: 0.8,\n",
       " 4: 0.5714285714285714,\n",
       " 5: 0.6666666666666666,\n",
       " 6: 0.5714285714285714,\n",
       " 7: 0.5714285714285714,\n",
       " 8: 0.7272727272727273}"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_10fold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0.16369047619047616,\n",
       " 1: 0.017857142857142856,\n",
       " 2: 0.10714285714285714,\n",
       " 3: 0.45535714285714285,\n",
       " 4: 0.0,\n",
       " 5: 0.011904761904761904,\n",
       " 6: 0.026785714285714284,\n",
       " 7: 0.11607142857142856,\n",
       " 8: 0.029761904761904757}"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bc_loo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0.6666666666666666,\n",
       " 1: 0.5333333333333333,\n",
       " 2: 0.6666666666666666,\n",
       " 3: 0.8,\n",
       " 4: 0.5,\n",
       " 5: 0.47058823529411764,\n",
       " 6: 0.5714285714285714,\n",
       " 7: 0.6153846153846154,\n",
       " 8: 0.5333333333333333}"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_loo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Betweeness centrality 10-fold')"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(bc_10fold.values())\n",
    "plt.title('Betweeness centrality 10-fold')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Closeness centrality 10-fold')"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(cc_10fold.values())\n",
    "plt.title('Closeness centrality 10-fold')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Betweeness centrality loo')"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(bc_loo.values())\n",
    "plt.title('Betweeness centrality loo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Closeness centrality loo')"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(cc_loo.values())\n",
    "plt.title('Closeness centrality loo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nodes of graph: \n",
      "[0, 1, 2, 3, 4, 5, 6, 7, 8]\n",
      "Edges of graph: \n",
      "[(0, 0), (0, 1), (0, 3), (0, 4), (0, 6), (0, 8), (1, 1), (1, 2), (1, 3), (1, 6), (2, 2), (2, 3), (2, 4), (2, 6), (2, 7), (2, 8), (3, 3), (3, 4), (3, 5), (3, 7), (3, 8), (4, 4), (4, 5), (4, 6), (4, 8), (5, 5), (5, 7), (5, 8), (6, 6), (6, 7), (6, 8), (7, 7), (7, 8), (8, 8)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#G=nx.path_graph(4)\n",
    "\n",
    "print(\"Nodes of graph: \")\n",
    "print(G_5fold.nodes())\n",
    "print(\"Edges of graph: \")\n",
    "print(G_5fold.edges())\n",
    "nx.draw(G_5fold)\n",
    "#plt.savefig(\"path_graph1.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import networkx as nx\n",
    "G=G_loo\n",
    "#G=nx.from_numpy_matrix(final_sim_kf5)\n",
    "pos=nx.spring_layout(G) # positions for all nodes\n",
    "\n",
    "# nodes\n",
    "nx.draw_networkx_nodes(G,pos,\n",
    "                       nodelist=[0,1,2,3,4,5,6,7,8],\n",
    "                       node_color='r',\n",
    "                       node_size=500,\n",
    "                   alpha=0.8)\n",
    "#nx.draw_networkx_nodes(G,pos,\n",
    "#                       nodelist=[4,5,6,7],\n",
    "#                       node_color='b',\n",
    "#                       node_size=500,\n",
    "#                   alpha=0.8)\n",
    "\n",
    "# edges\n",
    "nx.draw_networkx_edges(G,pos,width=1.0,alpha=0.5)\n",
    "nx.draw_networkx_edges(G,pos,\n",
    "                       width=2,alpha=0.5,edge_color='b')\n",
    "#nx.draw_networkx_edges(G,pos,\n",
    "#                       edgelist=[(4,5),(5,6),(6,7),(7,4)],\n",
    "#                       width=8,alpha=0.5,edge_color='b')\n",
    "\n",
    "#reliefF,lap_score,ll_l21,UDFS,fisher_score,chi_square,gini_index\n",
    "# some math labels\n",
    "labels={}\n",
    "labels[0]=r'reliefF'\n",
    "labels[1]=r'lap_score'\n",
    "labels[2]=r'll_l21'\n",
    "labels[3]=r'ls_l21'\n",
    "labels[4]=r'UDFS'\n",
    "labels[5]=r'fisher_score'\n",
    "labels[6]=r'chi_square'\n",
    "labels[7]=r'gini_index'\n",
    "labels[8]=r'SPEC'\n",
    "nx.draw_networkx_labels(G,pos,labels,font_size=12)\n",
    "\n",
    "#plt.axis('off')\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (5,5)\n",
    "#plt.savefig(\"5fold_graph.png\") # save as png\n",
    "#plt.figure(figsize=(10,10))\n",
    "plt.title('RH: loo graph')\n",
    "plt.show() # display"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stability construction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from helpers.similarities import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def kuncheva_stability(featidx,d):\n",
    "    q,k = featidx.shape\n",
    "    #q = size(featidx,1);\n",
    "    #q=num_methods\n",
    "    r = np.zeros((q,q))\n",
    "    for n in range(1,q-1):\n",
    "        for m in range(n,q-1):\n",
    "            r[n,m] = len(np.intersect1d(featidx[n,0],featidx[n,1])) + len(np.intersect1d(featidx[n,0],featidx[n + 1,1])) + len(np.intersect1d(featidx[n,0],featidx[n-1,1]))\n",
    "    A = np.true_divide(np.subtract(r, (k**2/d)),(k-(k**2/d)))\n",
    "    S = 2*sum(sum(A))/(q*(q-1))\n",
    "    return(S)\n",
    "\n",
    "def get_ranking_matrix(a,k):\n",
    "    #The columns are the respective rankings of pool_FS\n",
    "    #a=dataframe_ranking_5fold.loc[10]\n",
    "    #a=a.as_matrix(columns=None)\n",
    "    a=np.array(a)\n",
    "    b=np.zeros((k,len(pool_FS)))\n",
    "    for i in range(len(a)):\n",
    "        b[:,i]=a[i]\n",
    "    return(b)\n",
    "\n",
    "def normalize_stability(matrix):\n",
    "    n,m=matrix.shape\n",
    "    norm_mat = np.zeros((n,m))\n",
    "    for i in range(n):\n",
    "        for j in range(m):\n",
    "            norm_mat[i,j] = 100*(matrix[i,j] - np.min(matrix)) / (np.max(matrix) - np.min(matrix))\n",
    "    return(norm_mat)\n",
    "\n",
    "def get_stability(ranking_matrix):\n",
    "    \n",
    "    stab_mat=np.zeros((len(pool_FS),len(pool_FS)))\n",
    "    for i in range(len(pool_FS)):\n",
    "        for j in range(len(pool_FS)):\n",
    "            if i!=j:\n",
    "                stab_mat[i,j] = kuncheva_stability(np.transpose(np.vstack((ranking_matrix[:,i],ranking_matrix[:,j]))),595)\n",
    "    stab_mat=np.abs(stab_mat)\n",
    "    stab_mat=normalize_stability(stab_mat)\n",
    "    return(stab_mat)\n",
    "\n",
    "def rowStability(row):\n",
    "    matrix=get_ranking_matrix(row,row.name)\n",
    "    return(get_stability(matrix))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "pool_FS=[reliefF,lap_score,ll_l21,ls_l21,UDFS,fisher_score,chi_square,gini_index,SPEC]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ranking_5fold=dataframe_ranking_5fold.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ranking_10fold=dataframe_ranking_10fold.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ranking_loo=dataframe_ranking_loo.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ranking_5fold.drop(['overlapping'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ranking_10fold.drop(['overlapping'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ranking_loo.drop(['overlapping'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['reliefF', 'lap_score', 'll_l21', 'ls_l21', 'UDFS', 'fisher_score',\n",
       "       'chi_square', 'gini_index', 'SPEC'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ranking_5fold.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ranking_5fold['stability']=df_ranking_5fold.apply(rowStability,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ranking_10fold['stability']=df_ranking_10fold.apply(rowStability,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ranking_loo['stability']=df_ranking_loo.apply(rowStability,axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mean stability K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "average_5fold_stability=df_ranking_5fold.stability.mean()\n",
    "average_10fold_stability=df_ranking_10fold.stability.mean()\n",
    "average_loo_stability=df_ranking_loo.stability.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Store similarities for later use on Hypergraph Learning framework"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('RH_metrics/similarities/average_stability_5fold','wb') as f: pickle.dump(average_5fold_stability, f)\n",
    "with open('RH_metrics/similarities/average_stability_10fold','wb') as f: pickle.dump(average_10fold_stability, f)\n",
    "with open('RH_metrics/similarities/average_stability_loo','wb') as f: pickle.dump(average_loo_stability, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Uncomment this to read similarities"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "import pandas as pd \n",
    "average_5fold_stability=pd.read_pickle('RH_metrics/similarities/average_stability_5fold')\n",
    "average_10fold_stability=pd.read_pickle('RH_metrics/similarities/average_stability_10fold')\n",
    "average_loo_stability=pd.read_pickle('RH_metrics/similarities/average_stability_loo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of mean stability')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams[\"figure.figsize\"] = (10,10)\n",
    "plt.hist(average_5fold_stability)\n",
    "plt.title('Distribution of mean stability')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MinMaxScaler(copy=True, feature_range=(0, 1))\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "print(scaler.fit(average_5fold_stability))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "normalized_stability=scaler.transform(average_5fold_stability)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of mean stability')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (10,10)\n",
    "plt.hist(normalized_stability)\n",
    "plt.title('Distribution of mean stability')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'RH: Average 5-fold stability')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(average_5fold_stability,cmap=plt.cm.prism_r)\n",
    "plt.title('RH: Average 5-fold stability')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'RH: Average 10-fold stability')"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(average_10fold_stability,cmap=plt.cm.prism_r)\n",
    "plt.title('RH: Average 10-fold stability')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'RH: Average loo stability')"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAASsAAAE/CAYAAAAe4+U5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAVjUlEQVR4nO3de9RldX3f8feHgeFeMTKx3OTSWrywKtBHEsVQAqJg8LKiKBqJ0qRjskKUxi4qrhpjatPLclnpaqqdhYqJXMqgNNQYkFStcVWRh1vkZkQYZZCRQe4axJFv/9j70TMPz+U8eC7zY96vtc6afZ69z+/73fM853P23mefs1NVSNK2bodpNyBJwzCsJDXBsJLUBMNKUhMMK0lNMKwkNcGw0jYhyXlJ3j/tPhaT5NgkG5eY/5Ek71lo2SQ3JTl2Am0+pRlWI5RkQ5K/T/JIkk39E3CPgflPeEImOShJJdlxhbXOS7IlyT6j6n97kuSPknxyVONV1e9U1b9bZN7zq+qL46i7PTGsRu+VVbUHcDhwBHD2qAsk2R14LfAg8OZRj9/XWFF4SuNmWI1JVW0CrqALrVF7LfAA8MfAW+Z+mGTffsvuFwZ+dkSSe5Ps1N//F0luSXJ/kiuSHDiwbCX5vSTfBL7Z/+ycJHcmeSjJNUl+ZWD5XZN8oh/rliRnzdv92TfJp5JsTnJHkrcPu4JJ/mWS25Lcl+SyJPsOzHtxkquTPNj/++Ilxvk3Se5K8nCSbyQ5PsmJwLuBN/RbwTf0y57er8fDSW5P8rYFxnt3//+5IclvDPx80d3YftmXLlQ3ySlJrpm3/B8k+Yth/6+2F4bVmCTZHzgJuG2Fj3tTkr9dZrG3ABcCFwHPSfLPAKrqu8BX6MJszpuAS6rqx0leTfdk+XVgDfA3/TiDXgP8EvC8/v7VdIH7C8AFwPoku/Tz3gscBBwCnMDAVl6SHYD/DdwA7AccD5yZ5OVD/B8cB/wH4PXAPsC3+3WlD+K/BP4r8Azgg8BfJnnGAuMcCpwBvLCq9gReDmyoqsuBPwH+Z1XtUVUv6B9yD3Ay8A+A04H/kuTIgSH/IbB3vz5vAdb1NYaySN3LgIOTPHdg0dOAPxt23O1GVXkb0Q3YADwCPAwU8H+AvQbmnwc8SrdVNHd7qF92xyFrPAt4HDi8v38FcM7A/N8GPt9PB7gTOKa//1fAbw0suwPwQ+DA/n4Bxy1T/37gBf307cDL59Xe2E//EvCdeY89G/j4IuOeB7y/n/4o8J8H5u0B/JguGE8DvjbvsV8B3rrAmP+YLoBeCuw0b94fAZ9cZl3/F/COfvpYYAuw+8D8i4H3LND/sXP/DwN/Fy9drC7wYeDf99PP7/+Pd5723/O2dnPLavReU92r+LHAc+heiQd9oKr2mrsB/3SF458G3FJV1/f3zwfeNLebB3wKeFF/4P0YumD7m37egcA5SR5I8gBwH12g7Tcw/p2DxZL8637X6MH+MU8bWKd95y0/OH0gsO9crf6x7waeOcQ67ku3NQVAVT0CfL/vc6t5vW/PW4e5x90GnEkXEPckuWhwd3K+JCcl+Wq/6/kA8Aq2/v3dX1U/mFd30fFW4BN0v8PQ/X4vrqofjWDcpxTDakyq6v/Svdp+YMRD/yZwSP9u4ya63aC96Z5YVNX9wOeAN9DtAl5U/Us2XZi8bTAsq2rXqvp/g63PTfTHp86i2x17eh+uD9IFHMDdwP4Djz1gYPpO4I55tfasqlcMsY7fpQu7uT52p9vlu2v+vN6z+nlPUFUXVNVL+scU8J/mr2dfY2e6oP8A8Mx+XT87sK4AT+97Gaz73SHWZ6uWFujxq8BjwK/Q/c7+fIVjbhcMq/H6EHBCkhcsu+QQkrwI+EfAUXTHkQ4HDqM7lvSbA4vO3X9dPz3nI8DZSZ7fj/e0JKcsUXJPul2fzcCOSf6Q7njOnIv78Z6eZD+640NzvgY83B/g3jXJqiSHJXnhEKt6IXB6ksP7EPkT4Kqq2kAXIP+kP7a3Y5I30B1f+8z8QZIcmuS4foxHgb+n29IE+B5wUH9sDWA1sHO/rluSnAS8bIHe3pdkdR/kJwPrh1ifQfPrzvkz4L8BP66qL69wzO2CYTVGVbWZ7o/wD4d9TJLfSHLTIrPfAvxFVX29qjbN3YBzgJPzs3cBLwOeDWyqqhsG+rmUbsvioiQPATfSvQmwmCuAy4G/o9vleZStd/X+GNgI3AH8NXAJ8KO+1k/onsyH9/PvBc6l241cUlX9NfAeui2du+kC+tR+3vf7cd9Jt2t4FnByVd27wFA7A/+xr70J+EV+dirJXMh8P8m1VfUw8Ha6AL6fbgvnsnnjbernfZdu9/t3qurW5dZnnq3qDvz8z+leeDwHaxH52R6C9PNJ8rvAqVX1z6fdS2uS7Er3ZsCRVfXNafezLXLLSk9akn2SHJ1kh/4t/HcCl067r0b9LnC1QbU4z1LWz2M18D+Ag+lOw7gI+O9T7ahBSTbQHch/zZRb2aa5GyipCe4GSmqCYSWpCWM5ZrXz3qndDxrHyEs75Jrll3nK2W1KdX84pbqrp1T3sSnV3c5sAO6tykLzxhJWux8Ex189jpGXtn573E587vKLjMW0XhhG8eGWJ2PDlOrC9N4G2zL5kjNLzNsen96SGmRYSWqCYSWpCYaVpCYYVpKaYFhJaoJhJakJhpWkJhhWkpowVFglObG/5tptSd417qYkab5lwyrJKuBP6b7+9nnAG5M8b+lHSdJoDbNldRRwW1XdXlWP0X3B2qvH25YkbW2YsNqPrS8SsJEFrtEmSeM0sgPsSdYmmU0y+6PNoxpVkjrDhNVdbH3xyv1Z4IKSVbWuqmaqambnNaNqT5I6w4TV1cCzkxycZDXd9dvmX09NksZq2a/1qqotSc6gu+DlKuBjVbXYRTglaSyG+g7Cqvos3WW7JWkqPINdUhMMK0lNMKwkNcGwktQEw0pSEwwrSU0wrCQ1wbCS1ATDSlITDCtJTRjq4zYrdcg1sH4KMXjK45OvCdNZ1586fTpla3Y6dd+TB6dS9/08bSp1AdgyvdLbEresJDXBsJLUBMNKUhMMK0lNMKwkNcGwktQEw0pSEwwrSU0wrCQ1wbCS1IRlwyrJx5Lck+TGSTQkSQsZZsvqPODEMfchSUtaNqyq6kvAfRPoRZIW5TErSU0YWVglWZtkNsns5lENKkm9kYVVVa2rqpmqmlkzqkElqeduoKQmDHPqwoXAV4BDk2xM8lvjb0uStrbs1xpX1Rsn0YgkLcXdQElNMKwkNcGwktQEw0pSEwwrSU0wrCQ1wbCS1ATDSlITDCtJTTCsJDUhVTXyQWeSmh35qNuuUx6fXu31q6dUeMt0yu7C6P9eh/HoyzOVugBcMZ2yeeUUnsVfOo164OYF/7PdspLUBMNKUhMMK0lNMKwkNcGwktQEw0pSEwwrSU0wrCQ1wbCS1ATDSlITDCtJTRjmuoEHJPlCkpuT3JTkHZNoTJIGLXvdQLqPrL6zqq5NsidwTZIrq+rmMfcmST+17JZVVd1dVdf20w8DtwD7jbsxSRq0omNWSQ4CjgCuGkczkrSYocMqyR7Ap4Azq+qhBeavTTKbZHbzKDuUJIYMqyQ70QXV+VX16YWWqap1VTVTVTNrRtmhJDHcu4EBPgrcUlUfHH9LkvREw2xZHQ2cBhyX5Pr+9oox9yVJW1n21IWq+jIwxS+gliTPYJfUCMNKUhMMK0lNMKwkNcGwktQEw0pSEwwrSU0wrCQ1wbCS1ATDSlIThvmm0JXbDXjuWEZe2ulTqAmsXz2dugCnPDadur86pQ9gPfqn0ym8yxk1lboAH6nprHNlZuI1l6rolpWkJhhWkppgWElqgmElqQmGlaQmGFaSmmBYSWqCYSWpCYaVpCYYVpKaMMx1A3dJ8rUkNyS5Kcn7JtGYJA0a5rOBPwKOq6pH+iszfznJX1XVV8fcmyT91DDXDSzgkf7uTv1tep/qlLRdGuqYVZJVSa4H7gGurKqrxtuWJG1tqLCqqp9U1eHA/sBRSQ6bv0yStUlmk8xu3jLqNiVt71b0bmBVPQB8AThxgXnrqmqmqmbWjOdbsiRtx4Z5N3BNkr366V2BE4Bbx92YJA0aZhtoH+ATSVbRhdvFVfWZ8bYlSVsb5t3AvwWOmEAvkrQoz2CX1ATDSlITDCtJTTCsJDXBsJLUBMNKUhMMK0lNMKwkNcGwktQEw0pSE9J9t95ozSQ1O/JRlzeGVRlKMp26AM+8Zr+p1D3miLumUveqm/ecSt3vHPbwVOpOUw744eSLbjqaeuzaBZ9RbllJaoJhJakJhpWkJhhWkppgWElqgmElqQmGlaQmGFaSmmBYSWqCYSWpCUOHVX8J+euSeBkuSRO3ki2rdwC3jKsRSVrKUGGVZH/g14Bzx9uOJC1s2C2rDwFnAY+PsRdJWtSyYZXkZOCeqrpmmeXWJplNMrt5ZO1JUmeYLaujgVcl2QBcBByX5JPzF6qqdVU1U1Uza0bcpCQtG1ZVdXZV7V9VBwGnAp+vqjePvTNJGuB5VpKasONKFq6qLwJfHEsnkrQEt6wkNcGwktQEw0pSEwwrSU0wrCQ1wbCS1ATDSlITDCtJTTCsJDXBsJLUhBV93GZoq4F9xzLykp6TmnxR4NtTqdp59CuZSt0d7njdVOq+9tcvmUrdG6bzpwXAC3aaTt26c7eJ15xZYp5bVpKaYFhJaoJhJakJhpWkJhhWkppgWElqgmElqQmGlaQmGFaSmmBYSWrCUB+36S9w+jDwE2BLVS11VrwkjdxKPhv4q1V179g6kaQluBsoqQnDhlUBn0tyTZK142xIkhYy7G7gS6rqriS/CFyZ5Naq+tLgAn2IrQV41qoRdylpuzfUllVV3dX/ew9wKXDUAsusq6qZqppZY1hJGrFlwyrJ7kn2nJsGXgbcOO7GJGnQMLuBzwQuTTK3/AVVdflYu5KkeZYNq6q6HXjBBHqRpEV56oKkJhhWkppgWElqgmElqQmGlaQmGFaSmmBYSWqCYSWpCYaVpCYYVpKasJJvCh3eY8CGsYy8pH/LdZMvCpz28iOnUhdglzNqKnUfJ1Ope+Z0Vpf3T6kuwPot06u9LXHLSlITDCtJTTCsJDXBsJLUBMNKUhMMK0lNMKwkNcGwktQEw0pSEwwrSU0YKqyS7JXkkiS3JrklyYvG3ZgkDRr2s4HnAJdX1euSrAZ2G2NPkvQEy4ZVkqcBxwBvBaiqx+g+qixJEzPMbuDBwGbg40muS3Jufxl5SZqYYcJqR+BI4MNVdQTwA+Bd8xdKsjbJbJLZzSNuUpKGCauNwMaquqq/fwldeG2lqtZV1UxVzawZZYeSxBBhVVWbgDuTHNr/6Hjg5rF2JUnzDPtu4O8D5/fvBN4OnD6+liTpiYYKq6q6HpgZcy+StCjPYJfUBMNKUhMMK0lNMKwkNcGwktQEw0pSEwwrSU0wrCQ1wbCS1ATDSlITUlUjH3QmqdlhP3U4SlumUHPKzhv9r28ob8106g79adZRm+Lf1imPT6fu+h1eP/GaM1zJbN234F+XW1aSmmBYSWqCYSWpCYaVpCYYVpKaYFhJaoJhJakJhpWkJhhWkppgWElqgmElqQnLhlWSQ5NcP3B7KMmZk2hOkuYs+7HQqvoGcDhAklXAXcClY+5Lkray0t3A44FvVdW3x9GMJC1mpWF1KnDhOBqRpKUMHVZJVgOvAtYvMn9tktkks5tH1Z0k9VayZXUScG1VfW+hmVW1rqpmqmpmzWh6k6SfWklYvRF3ASVNyVBhlWR34ATg0+NtR5IWNtQ3WlfVD4BnjLkXSVqUZ7BLaoJhJakJhpWkJhhWkppgWElqgmElqQmGlaQmGFaSmmBYSWqCYSWpCUN93GbF9gZeM5aRl3buFGoCeeXsdAoDlZmp1M0BP5xK3bpzt6nUnab1O7x+KnVPefziide8/YWLz3PLSlITDCtJTTCsJDXBsJLUBMNKUhMMK0lNMKwkNcGwktQEw0pSEwwrSU0Y9lJc/yrJTUluTHJhkl3G3ZgkDVo2rJLsB7wdmKmqw4BVwKnjbkySBg27G7gjsGuSHYHdgO+OryVJeqJlw6qq7gI+AHwHuBt4sKo+N+7GJGnQMLuBTwdeDRwM7AvsnuTNCyy3NslsktnNj46+UUnbt2F2A18K3FFVm6vqx8CngRfPX6iq1lXVTFXNrPHwu6QRGyasvgP8cpLdkgQ4HrhlvG1J0taGOWZ1FXAJcC3w9f4x68bclyRtZaivNa6q9wLvHXMvkrQoz2CX1ATDSlITDCtJTTCsJDXBsJLUBMNKUhMMK0lNMKwkNcGwktQEw0pSE1JVox802Qx8+0k+fG/g3hG2Y13rTru2dYd3YFWtWWjGWMLq55FktqpmrGvdp0pt646Gu4GSmmBYSWrCthhW0/quLOs+tetOs7Z1R2CbO2YlSQvZFresJOkJtpmwSnJikm8kuS3JuyZY92NJ7kly46Rq9nUPSPKFJDf3V7t+x4Tq7pLka0lu6Ou+bxJ1B+qvSnJdks9MsOaGJF9Pcn2S2QnW3SvJJUluTXJLkhdNqO6h/brO3R5KcuaEao/v6u1VNfUb3VWevwUcAqwGbgCeN6HaxwBHAjdOeJ33AY7sp/cE/m4S6wwE2KOf3gm4CvjlCa73HwAXAJ+ZYM0NwN6T/P32dT8B/HY/vRrYawo9rAI20Z2/NO5a+wF3ALv29y8G3jqq8beVLaujgNuq6vaqegy4iO5ahWNXVV8C7ptErXl1766qa/vph+muGLTfBOpWVT3S392pv03kwGWS/YFfA86dRL1pSvI0uhfCjwJU1WNV9cAUWjke+FZVPdmTtFdqbFdv31bCaj/gzoH7G5nAE3dbkeQg4Ai6rZxJ1FuV5HrgHuDK6q5gNAkfAs4CHp9QvTkFfC7JNUnWTqjmwcBm4OP9bu+5SXafUO1BpwIXTqJQjfnq7dtKWG23kuwBfAo4s6oemkTNqvpJVR0O7A8cleSwcddMcjJwT1VdM+5aC3hJVR0JnAT8XpJjJlBzR7rDCx+uqiOAHwATOxYLkGQ18Cpg/YTqDXX19idrWwmru4ADBu7v3//sKS3JTnRBdX5VfXrS9fvdki8AJ06g3NHAq5JsoNvNPy7JJydQd+4Vn6q6B7iU7rDDuG0ENg5stV5CF16TdBJwbVV9b0L1hrp6+5O1rYTV1cCzkxzcvxqcClw25Z7Gqr+69UeBW6rqgxOsuybJXv30rsAJwK3jrltVZ1fV/lV1EN3v9/NVNbJX3cUk2T3JnnPTwMuAsb/zW1WbgDuTHNr/6Hjg5nHXneeNTGgXsDfWq7cPdZHTcauqLUnOAK6ge/fiY1V10yRqJ7kQOBbYO8lG4L1V9dEJlD4aOA34en/8CODdVfXZMdfdB/hEklV0L1YXV9XETiOYgmcCl3bPHXYELqiqyydU+/eB8/sX4NuB0ydUdy6YTwDeNqmaVXVVkrmrt28BrmOEZ7N7BrukJmwru4GStCTDSlITDCtJTTCsJDXBsJLUBMNKUhMMK0lNMKwkNeH/A95eKIW/a9/TAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(average_loo_stability,cmap=plt.cm.prism_r)\n",
    "plt.title('RH: Average loo stability')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Similarity with stability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[20.        , 17.93721973, 20.        , 20.        , 20.        ,\n",
       "        20.        , 20.        , 20.        , 20.        ],\n",
       "       [17.93721973, 20.        , 20.        , 10.03344482, 20.        ,\n",
       "        17.04545455, 20.        , 12.5       , 15.50387597],\n",
       "       [20.        , 20.        , 20.        , 11.26760563, 20.        ,\n",
       "        20.        , 20.        , 14.47527141, 18.66251944],\n",
       "       [20.        , 10.03344482, 11.26760563, 20.        , 12.25740552,\n",
       "        20.        , 10.83032491, 20.        , 20.        ],\n",
       "       [20.        , 20.        , 20.        , 12.25740552, 20.        ,\n",
       "        20.        , 20.        , 16.15074024, 20.        ],\n",
       "       [20.        , 17.04545455, 20.        , 20.        , 20.        ,\n",
       "        20.        , 19.48051948, 20.        , 20.        ],\n",
       "       [20.        , 20.        , 20.        , 10.83032491, 20.        ,\n",
       "        19.48051948, 20.        , 13.76146789, 17.49271137],\n",
       "       [20.        , 12.5       , 14.47527141, 20.        , 16.15074024,\n",
       "        20.        , 13.76146789, 20.        , 20.        ],\n",
       "       [20.        , 15.50387597, 18.66251944, 20.        , 20.        ,\n",
       "        20.        , 17.49271137, 20.        , 20.        ]])"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_sim_matrix_5fold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "Final_sim_5fold_stability=np.multiply(acc_sim_matrix_5fold,average_similarity_5fold,average_5fold_stability)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "Final_sim_10fold_stability=np.multiply(acc_sim_matrix_10fold,average_similarity_10fold,average_10fold_stability)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "Final_sim_loo_stability=np.multiply(acc_sim_matrix_loo,average_similarity_loo,average_loo_stability)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'RH: Final similarity matrice with stability 5fold')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjsAAAJOCAYAAABROcYpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAfr0lEQVR4nO3de7hld13f8c+XDAQSkKBECklKUDGCqARHrhaRCIVy1RoNFhS0jdqioFAKtBWwtbZP0aotalNuodwkkVhKEYJCuChGJiFcQtBCQHKDDFAgCZAL+faPvSYcDnPm7JnZ++zJ77xezzNPzjl77bW+61zfe621d6q7AwAwqlusegAAgGUSOwDA0MQOADA0sQMADE3sAABDEzsAwNDEDttCVV1dVd+2gPU8v6peeQD3u7CqHnKA2/zTqvqZ6e0nV9W7D2Q90/2fW1UvPtD7r1pV/ZOqOvtQ3HZVPaSqLt3KmabtHl9VXVU7Nrj9pq/5+mXXfm9twZw/WlWXTD+LJ26y7Mur6t/v4/auqu9Y/JSMSuywJarqE1X15ekX3aemX2a3XXP7N/xy2+yX+F62cU5VfWXaxp5/D0iS7r5td1+82L2aX3d/d3efc4D3fWR3n76gOf5Dd//TZP8/v8s07yzd/arufvhWzbWvbS/rD+7BBu16a7/me7ntpu+tBYT086vq+nU/f2sfYLwwyVOnn8X3Heh24ECIHbbSY7r7tknuneTEJM9Zwjb2/DLd8+89S9jGzdKhEDUH4+Y+/zbxR+t+/tY+wLhrkgtXNRjbm9hhy3X3p5K8JbPo2RJrH4VPR5FeVFX/p6quqqpzq+rb1yz7u9Ph9i9W1XlV9Q/m3MYdq+qNVfX5qvpcVb2rqm4x3faJqvqR6e3nV9UZVfXKafsfrKrvrKrnVNWV07bXHkE4p6r2+sh8X7NO2zlz2s4Xkzx53Wm4d07//fz0KPyHprm/Z806vrWqvlRVR+9l20+uqr+oqv8y7fPFVfXA6eOXTPvyM2uWf1RVvW+a9ZKqev6a1a2f5QHr1v/ZJM9ff/Shqr67qt46zf3pqnru9PFbVNWzq+pjVfXZqnpdVX3zBp/Dd1TVP57eftD0vfKo6f2TquqCNfv77untPfO+f5r3J9es7xnTvl9RVU/Z2zbXrO/i6Xvg4zU7TXaPJH+Y5AHTej8/x+duj5+tqsun7T5zzXY2PPW653trb9utqh+YPqeHrVn+x6rq/Rvt0wbbOLyqrk5y2PT5+tj08XtM2/98zU7zPnYf6/iX035dXlU/uz/bh0TssAJVdWySRyb56H7e76eq6gMLGuOUJC9Icodpjt9Yc9t7Mwuxb07y6iRnVNWt51jnM5JcmuToJHdK8twkG/3/WB6T5H9O239fZvF3iyTHJPn1JP99zv3YbNbHJTkzyVFJXrXuvg+e/nvU9Cj8HUlem+SJa5Z5QpI/7+7dG2z/fkk+kORbpu2/NskPJPmOaT3/rb52uvKaJD89zfKoJL9YVY/fYJb3rFn/xZl9Ptd+jVJVt0vyZ0nenOQu0zb/fLr5l5I8PskPTbf9vyQv2mAf3pHkIdPbPzRt78Fr3n/H+jt0957bv2+a94+m9/9ekttn9nX8uSQvqqo7rL9/VR2Z5PeSPLK7b5fkgUku6O6LkvxCkvdM6z1qusu+Pnd7/HCSuyd5eJJ/VVNcz2Nv2+3u9yb57LS+PZ6U5BX7WNVjpvC8sKp+cVr3tdMR3WT2+fr2qrplkv+d5Owk35rZ1+tVVXXC+hVW1SOSPDPJw6b9m3u/YA+xw1b6k6q6KsklSa5M8rx1tz9zepT3+ekR7deFTXe/uru/d5Nt/N6adZy/j+XO6u6/7u4bMouAm44ydfcru/uz3X1Dd/9WksOTfMMv4b24Psmdk9y1u6/v7nf1xv/zuXd191um7Z+RWSD9x+6+PrNgOL6qjtrgvjeZY9b3dPefdPeN3f3lOfbh9CRPqKqa3n9SZlG2kY9398u6+6tJ/ijJcUl+ffoDd3aS6zKLkHT3Od39wWmWDyR5TWYxsS+Xd/d/nfZv/fyPTvKp7v6t7v5Kd1/V3edOt/1Ckn/d3Zd297VJnp/kx2vvp8LesWaOByf5zTXv7zV29uH6zPb/+u5+U5Krs/H3zo1J7lVVt+nuK7p7w1M8c37uXtDd13T3B5O8LLNQPVinZ4rf6cjYP8wsavfmdUnukdn38j9L8mtVtdEM909y28y+56/r7rcleeMGM/9Ekpd194e6+5rMvpawX8QOW+nx06PYhyT5riR3XHf7C6dHlEdNj2g3C5u9+eU167jPPpb71Jq3v5TZL94kSVU9s6ouqqovTNF1+73Mujf/ObOjRGdPpyeevY9lP73m7S8n+cwUDHvez9qZNjLHrJfMMfdNplj4UpKHVNV3ZRYqb9jHXdbvR7p7/cduO816v6p6e1XtrqovZBYkm31e9zX/cUk+tsFtd01y1ppwvijJVzM7QrTee5J8Z1XdKbPofUWS46rqjknum6+dYpvHZ6eA3ePrvrf2mP5o/2Rmn4MranZK9bs2Wumcn7u1n6u/y+yI1sF6ZWZHa47MLDre1d1X7G3B7v5wd1/e3V/t7r9M8rtJfnyD9d4lySXdfeO6mY/ZaNl1y8F+ETtsuel0ycsze3bGIaVm17w8K7Nf7HeYousLSWqfd0wyHVl4Rnd/W5LHJvnVqjppxbNudGRpX7fteTT/pCRndvdXFjBuMjsi8IYkx3X37TO7RmTPrBvNsq/5L0my0csJXJLZKaKj1vy7dXdf9g0b6P5SkvOSPC3Jh7r7uiR/meRXk3ysuz+z2Y4diOnI3sMyOxr4kST/Y89Ne1l8X5+7PY5b8/bfT3L5/o60lxkvyywGfyybH+Xb2/o2+rm5PLOgXPs36O8n+YavT5Ir8o37BvtF7LAqv5PkYVX1faseZJ3bJbkhye4kO6rq15J80zx3rKpHV9V3TKeAvpDZkYQbN7nbwTjgWSe7M5tvfTC8MsmPZhY8+7o+Y3/dLsnnuvsrVXXfJD81xyz78sYkd66qp08Xwd6uqu433faHSX6jqu6aJFV1dFU9bh/rekeSp+Zrp6zOWff+3nx6P+e9SVXdqaoeNx0xuTaz0117vlc+neTYqrrVmrvs63O3x7+tqiOq6ruTPCWz04r7Y2/bTWbfA89K8j1JXr+PfXpcVd2hZu6b5JeT/K8NFt9zBPFZVXXLmr0G1WMyO4W73usyu7j+nlV1RL7x9DdsSuywEtMFr69I8mvz3qdmz1ZZ9lNX35LZBa9/m9nh8q9k/lNBd8/sgtmrM3s0/Pvd/fZlDDk5mFn3HNH4jSR/MZ3uuf/08UuSnJ/ZI/N3LXDef57k16frtn4tsz9i+5xlk/mvyuyi1cdkdlry/2Z2kW4yO4XyhsxOKV6V5K8yu9h5I+/ILCjeucH7e/P8JKdP8/7EZvOuc4vMjhxdnuRzmV1/84vTbW/L7Cnan6qqPUeVNvzcrduHj2Z2kfYLp2um9sfetpskZ2U6LTh9nTZyyrT9qzL72f5PvcHrQ01Hzx6T2RMVPpPk95P8dHd/ZC/L/mlmD47eNq3/bfu5X5Da+PpJYLuqqpdmdnHwv1n1LKze9HTxn+/uP1v1LHAgvEgX8HWq6vjMrtHY50v6sz3U7DWIOo6ocDMmdoCbVNW/S/IrSX6zuz++6nlYrao6J8k9kzxp3TOn4GbFaSwAYGguUAYAhraU01h3rOrjl7HiQ9TF37/qCQD2321WPcAWm+clxLn5uuYTybWf6b2+ttNSYuf4JLuWseJD1MnvXfUEAPvvezd9qcyxfMBVG0P78x/Y+DansQCAoYkdAGBoYgcAGJrYAQCGJnYAgKGJHQBgaGIHABia2AEAhiZ2AIChiR0AYGhiBwAYmtgBAIYmdgCAoYkdAGBoYgcAGJrYAQCGJnYAgKGJHQBgaGIHABia2AEAhiZ2AIChiR0AYGhiBwAYmtgBAIYmdgCAoc0VO1X1iKr6m6r6aFU9e9lDAQAsyqaxU1WHJXlRkkcmuWeSJ1TVPZc9GADAIsxzZOe+ST7a3Rd393VJXpvkccsdCwBgMeaJnWOSXLLm/Uunj32dqjq1qnZV1a7di5oOAOAgLewC5e4+rbt3dvfOoxe1UgCAgzRP7FyW5Lg17x87fQwA4JA3T+y8N8ndq+puVXWrJKckecNyxwIAWIwdmy3Q3TdU1VOTvCXJYUle2t0XLn0yAIAF2DR2kqS735TkTUueBQBg4byCMgAwNLEDAAxN7AAAQxM7AMDQxA4AMDSxAwAMTewAAEMTOwDA0MQOADA0sQMADE3sAABDEzsAwNDEDgAwNLEDAAxN7AAAQxM7AMDQxA4AMDSxAwAMTewAAEMTOwDA0MQOADA0sQMADE3sAABDEzsAwNB2LGOlF39/cvJ7l7HmQ9MZ2zAZT75x1RMAB+vEVQ+wxT6w6gFYmW34ZxoA2E7EDgAwNLEDAAxN7AAAQxM7AMDQxA4AMDSxAwAMTewAAEMTOwDA0MQOADA0sQMADE3sAABDEzsAwNDEDgAwNLEDAAxN7AAAQxM7AMDQxA4AMDSxAwAMTewAAEMTOwDA0MQOADA0sQMADE3sAABDEzsAwNDEDgAwNLEDAAxt09ipqpdW1ZVV9aGtGAgAYJHmObLz8iSPWPIcAABLsWnsdPc7k3xuC2YBAFi4hV2zU1WnVtWuqtp17e5FrRUA4OAsLHa6+7Tu3tndOw8/elFrBQA4OJ6NBQAMTewAAEOb56nnr0nyniQnVNWlVfVzyx8LAGAxdmy2QHc/YSsGAQBYBqexAIChiR0AYGhiBwAYmtgBAIYmdgCAoYkdAGBoYgcAGJrYAQCGJnYAgKGJHQBgaGIHABia2AEAhiZ2AIChiR0AYGhiBwAYmtgBAIYmdgCAoYkdAGBoYgcAGJrYAQCGJnYAgKGJHQBgaGIHABia2AEAhrZj1QOM4OQbVz3B1jtju2XyRaseYOudfMKqJ2DZTu9VTwBbY7v9yQIAthmxAwAMTewAAEMTOwDA0MQOADA0sQMADE3sAABDEzsAwNDEDgAwNLEDAAxN7AAAQxM7AMDQxA4AMDSxAwAMTewAAEMTOwDA0MQOADA0sQMADE3sAABDEzsAwNDEDgAwNLEDAAxN7AAAQxM7AMDQxA4AMDSxAwAMbdPYqarjqurtVfXhqrqwqp62FYMBACzCjjmWuSHJM7r7/Kq6XZLzquqt3f3hJc8GAHDQNj2y091XdPf509tXJbkoyTHLHgwAYBH265qdqjo+yYlJzt3LbadW1a6q2nXt7sUMBwBwsOaOnaq6bZI/TvL07v7i+tu7+7Tu3tndOw8/epEjAgAcuLlip6pumVnovKq7X7/ckQAAFmeeZ2NVkpckuai7f3v5IwEALM48R3YelORJSR5aVRdM//7RkucCAFiITZ963t3vTlJbMAsAwMJ5BWUAYGhiBwAYmtgBAIYmdgCAoYkdAGBoYgcAGJrYAQCGJnYAgKGJHQBgaGIHABia2AEAhiZ2AIChiR0AYGhiBwAYmtgBAIYmdgCAoYkdAGBoYgcAGJrYAQCGJnYAgKGJHQBgaGIHABia2AEAhiZ2AICh7Vj1ANxMXbTqAbbYPVY9wArcuOoBABbDkR0AYGhiBwAYmtgBAIYmdgCAoYkdAGBoYgcAGJrYAQCGJnYAgKGJHQBgaGIHABia2AEAhiZ2AIChiR0AYGhiBwAYmtgBAIYmdgCAoYkdAGBoYgcAGJrYAQCGJnYAgKGJHQBgaGIHABia2AEAhiZ2AIChiR0AYGhiBwAYmtgBAIa2aexU1a2r6q+r6v1VdWFVvWArBgMAWIQdcyxzbZKHdvfVVXXLJO+uqj/t7r9a8mwAAAdt09jp7k5y9fTuLad/vcyhAAAWZa5rdqrqsKq6IMmVSd7a3efuZZlTq2pXVe26dveixwQAODBzxU53f7W7753k2CT3rap77WWZ07p7Z3fvPPzoRY8JAHBg9uvZWN39+SRvT/KI5YwDALBY8zwb6+iqOmp6+zZJHpbkI8seDABgEeZ5Ntadk5xeVYdlFkev6+43LncsAIDFmOfZWB9IcuIWzAIAsHBeQRkAGJrYAQCGJnYAgKGJHQBgaGIHABia2AEAhiZ2AIChiR0AYGhiBwAYmtgBAIYmdgCAoYkdAGBoYgcAGJrYAQCGJnYAgKGJHQBgaGIHABia2AEAhiZ2AIChiR0AYGhiBwAYmtgBAIYmdgCAoYkdAGBoO1Y9ADdPJ5+w6gm22I2rHmDrnbHNHgqdvA2/xrBdbLNfZwDAdiN2AIChiR0AYGhiBwAYmtgBAIYmdgCAoYkdAGBoYgcAGJrYAQCGJnYAgKGJHQBgaGIHABia2AEAhiZ2AIChiR0AYGhiBwAYmtgBAIYmdgCAoYkdAGBoYgcAGJrYAQCGJnYAgKGJHQBgaGIHABia2AEAhiZ2AIChzR07VXVYVb2vqt64zIEAABZpf47sPC3JRcsaBABgGeaKnao6Nsmjkrx4ueMAACzWvEd2fifJs5LcuNECVXVqVe2qql3X7l7IbAAAB23T2KmqRye5srvP29dy3X1ad+/s7p2HH72w+QAADso8R3YelOSxVfWJJK9N8tCqeuVSpwIAWJBNY6e7n9Pdx3b38UlOSfK27n7i0icDAFgAr7MDAAxtx/4s3N3nJDlnKZMAACyBIzsAwNDEDgAwNLEDAAxN7AAAQxM7AMDQxA4AMDSxAwAMTewAAEMTOwDA0MQOADA0sQMADE3sAABDEzsAwNDEDgAwNLEDAAxN7AAAQxM7AMDQxA4AMDSxAwAMTewAAEMTOwDA0MQOADA0sQMADE3sAABD27HqAYBD08k3rnqCrXXGNnzot92+xmxf2/DHGwDYTsQOADA0sQMADE3sAABDEzsAwNDEDgAwNLEDAAxN7AAAQxM7AMDQxA4AMDSxAwAMTewAAEMTOwDA0MQOADA0sQMADE3sAABDEzsAwNDEDgAwNLEDAAxN7AAAQxM7AMDQxA4AMDSxAwAMTewAAEMTOwDA0MQOADC0HfMsVFWfSHJVkq8muaG7dy5zKACARZkrdiY/3N2fWdokAABL4DQWADC0eWOnk5xdVedV1al7W6CqTq2qXVW169rdixsQAOBgzHsa6we7+7Kq+tYkb62qj3T3O9cu0N2nJTktSb55Z/WC5wQAOCBzHdnp7sum/16Z5Kwk913mUAAAi7Jp7FTVkVV1uz1vJ3l4kg8tezAAgEWY5zTWnZKcVVV7ln91d795qVMBACzIprHT3Rcn+b4tmAUAYOE89RwAGJrYAQCGJnYAgKGJHQBgaGIHABia2AEAhiZ2AIChiR0AYGhiBwAYmtgBAIYmdgCAoYkdAGBoYgcAGJrYAQCGJnYAgKGJHQBgaGIHABia2AEAhiZ2AIChiR0AYGhiBwAYmtgBAIYmdgCAoYkdAGBoYgcAGNqOZaz0Nkm+t5ax5kPTiaseYAVO71VPAIt18o2rnmDrnbHNHu5ux68xM9vsWx0A2G7EDgAwNLEDAAxN7AAAQxM7AMDQxA4AMDSxAwAMTewAAEMTOwDA0MQOADA0sQMADE3sAABDEzsAwNDEDgAwNLEDAAxN7AAAQxM7AMDQxA4AMDSxAwAMTewAAEMTOwDA0MQOADA0sQMADE3sAABDEzsAwNDEDgAwtLlip6qOqqozq+ojVXVRVT1g2YMBACzCjjmX+90kb+7uH6+qWyU5YokzAQAszKaxU1W3T/LgJE9Oku6+Lsl1yx0LAGAx5jmNdbcku5O8rKreV1Uvrqoj1y9UVadW1a6q2nXN7oXPCQBwQOaJnR1J7pPkD7r7xCTXJHn2+oW6+7Tu3tndO488esFTAgAcoHli59Ikl3b3udP7Z2YWPwAAh7xNY6e7P5Xkkqo6YfrQSUk+vNSpAAAWZN5nY/1SkldNz8S6OMlTljcSAMDizBU73X1Bkp1LngUAYOG8gjIAMDSxAwAMTewAAEMTOwDA0MQOADA0sQMADE3sAABDEzsAwNDEDgAwNLEDAAxN7AAAQxM7AMDQxA4AMDSxAwAMTewAAEMTOwDA0MQOADA0sQMADE3sAABDEzsAwNDEDgAwNLEDAAxN7AAAQxM7AMDQdixjpV9O8oFexpoPTR9Y9QAAB+DkG1c9wdY6Yxs+vN9uX+ONbMMvPQCwnYgdAGBoYgcAGJrYAQCGJnYAgKGJHQBgaGIHABia2AEAhiZ2AIChiR0AYGhiBwAYmtgBAIYmdgCAoYkdAGBoYgcAGJrYAQCGJnYAgKGJHQBgaGIHABia2AEAhiZ2AIChiR0AYGhiBwAYmtgBAIYmdgCAoYkdAGBom8ZOVZ1QVRes+ffFqnr6VgwHAHCwdmy2QHf/TZJ7J0lVHZbksiRnLXkuAICF2N/TWCcl+Vh3/90yhgEAWLT9jZ1TkrxmbzdU1alVtauqdl27++AHAwBYhLljp6puleSxSc7Y2+3dfVp37+zunYcfvajxAAAOzv4c2XlkkvO7+9PLGgYAYNH2J3aekA1OYQEAHKrmip2qOjLJw5K8frnjAAAs1qZPPU+S7r4mybcseRYAgIXzCsoAwNDEDgAwNLEDAAxN7AAAQxM7AMDQxA4AMDSxAwAMTewAAEMTOwDA0MQOADA0sQMADE3sAABDEzsAwNDEDgAwNLEDAAxN7AAAQxM7AMDQxA4AMDSxAwAMTewAAEMTOwDA0MQOADA0sQMADE3sAABD27HqAQBgK5x846on2HpnbKNDGjv3cds2+jQAANuR2AEAhiZ2AIChiR0AYGhiBwAYmtgBAIYmdgCAoYkdAGBoYgcAGJrYAQCGJnYAgKGJHQBgaGIHABia2AEAhiZ2AIChiR0AYGhiBwAYmtgBAIYmdgCAoYkdAGBoYgcAGJrYAQCGJnYAgKGJHQBgaGIHABia2AEAhiZ2AIChzRU7VfUrVXVhVX2oql5TVbde9mAAAIuwaexU1TFJfjnJzu6+V5LDkpyy7MEAABZh3tNYO5Lcpqp2JDkiyeXLGwkAYHE2jZ3uvizJC5N8MskVSb7Q3WevX66qTq2qXVW169rdix8UAOBAzHMa6w5JHpfkbknukuTIqnri+uW6+7Tu3tndOw8/evGDAgAciHlOY/1Iko939+7uvj7J65M8cLljAQAsxjyx88kk96+qI6qqkpyU5KLljgUAsBjzXLNzbpIzk5yf5IPTfU5b8lwAAAuxY56Fuvt5SZ635FkAABbOKygDAEMTOwDA0MQOADA0sQMADE3sAABDEzsAwNDEDgAwNLEDAAxN7AAAQxM7AMDQxA4AMDSxAwAMTewAAEMTOwDA0MQOADA0sQMADE3sAABDEzsAwNDEDgAwNLEDAAxN7AAAQxM7AMDQxA4AMDSxAwAMrbp78Sut2p3k7xa+4s3dMclnVrDdVdlu+5tsv322v+Pbbvu83fY32X77vKr9vWt3H723G5YSO6tSVbu6e+eq59gq221/k+23z/Z3fNttn7fb/ibbb58Pxf11GgsAGJrYAQCGNlrsnLbqAbbYdtvfZPvts/0d33bb5+22v8n22+dDbn+HumYHAGC90Y7sAAB8HbEDAAxtiNipqkdU1d9U1Uer6tmrnmfZquqlVXVlVX1o1bNshao6rqreXlUfrqoLq+ppq55p2arq1lX111X1/mmfX7DqmbZCVR1WVe+rqjeuepZlq6pPVNUHq+qCqtq16nm2QlUdVVVnVtVHquqiqnrAqmdalqo6Yfra7vn3xap6+qrnWqaq+pXp99WHquo1VXXrVc+0x83+mp2qOizJ3yZ5WJJLk7w3yRO6+8MrHWyJqurBSa5O8oruvteq51m2qrpzkjt39/lVdbsk5yV5/OBf40pyZHdfXVW3TPLuJE/r7r9a8WhLVVW/mmRnkm/q7kevep5lqqpPJNnZ3dvmxeaq6vQk7+ruF1fVrZIc0d2fX/Vcyzb9nbosyf26exUvuLt0VXVMZr+n7tndX66q1yV5U3e/fLWTzYxwZOe+ST7a3Rd393VJXpvkcSueaam6+51JPrfqObZKd1/R3edPb1+V5KIkx6x2quXqmaund285/bt5PzLZRFUdm+RRSV686llYvKq6fZIHJ3lJknT3ddshdCYnJfnYqKGzxo4kt6mqHUmOSHL5iue5yQixc0ySS9a8f2kG/0O4nVXV8UlOTHLuaidZvumUzgVJrkzy1u4efZ9/J8mzkty46kG2SCc5u6rOq6pTVz3MFrhbkt1JXjadqnxxVR256qG2yClJXrPqIZapuy9L8sIkn0xyRZIvdPfZq53qa0aIHbaJqrptkj9O8vTu/uKq51m27v5qd987ybFJ7ltVw56yrKpHJ7myu89b9Sxb6Ae7+z5JHpnkX0ynp0e2I8l9kvxBd5+Y5Jok2+Eay1sleWySM1Y9yzJV1R0yO6tytyR3SXJkVT1xtVN9zQixc1mS49a8f+z0MQYyXbfyx0le1d2vX/U8W2k61P/2JI9Y9SxL9KAkj52uY3ltkodW1StXO9JyTY+E091XJjkrs1PyI7s0yaVrjlCemVn8jO6RSc7v7k+vepAl+5EkH+/u3d19fZLXJ3ngime6yQix894kd6+qu00FfUqSN6x4JhZoulj3JUku6u7fXvU8W6Gqjq6qo6a3b5PZBfgfWe1Uy9Pdz+nuY7v7+Mx+ht/W3YfMo8JFq6ojp4vtM53KeXiSoZ9d2d2fSnJJVZ0wfeikJMM+yWCNJ2TwU1iTTya5f1UdMf3OPimz6ysPCTtWPcDB6u4bquqpSd6S5LAkL+3uC1c81lJV1WuSPCTJHavq0iTP6+6XrHaqpXpQkicl+eB0DUuSPLe737TCmZbtzklOn57FcYskr+vu4Z+OvY3cKclZs78J2ZHk1d395tWOtCV+KcmrpgemFyd5yornWaopZB+W5OdXPcuydfe5VXVmkvOT3JDkfTmE/rcRN/unngMA7MsIp7EAADYkdgCAoYkdAGBoYgcAGJrYAQCGJnYAgKGJHQBgaP8fwHJ2ARvAALcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(Final_sim_5fold_stability,cmap=plt.cm.prism_r)\n",
    "plt.title('RH: Final similarity matrice with stability 5fold')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'RH:Final similarity matrice with stability 10-fold')"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(Final_sim_10fold_stability,cmap=plt.cm.prism_r)\n",
    "plt.title('RH:Final similarity matrice with stability 10-fold')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'RH:Final similarity matrice with stability loo')"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(Final_sim_loo_stability,cmap=plt.cm.prism_r)\n",
    "plt.title('RH:Final similarity matrice with stability loo')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Centrality  with stability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "G_5fold_stability=nx.from_numpy_matrix(Final_sim_5fold_stability)\n",
    "G_10fold_stability=nx.from_numpy_matrix(Final_sim_10fold_stability)\n",
    "G_loo_stability=nx.from_numpy_matrix(Final_sim_loo_stability)\n",
    "#centrality_5fold_stability=nx.degree_centrality(G_5fold_stability)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "cc_5fold=nx.closeness_centrality(G_5fold_stability)\n",
    "bc_5fold=nx.betweenness_centrality(G_5fold_stability)\n",
    "cc_10fold=nx.closeness_centrality(G_10fold_stability)\n",
    "bc_10fold=nx.betweenness_centrality(G_10fold_stability)\n",
    "cc_loo=nx.closeness_centrality(G_loo_stability)\n",
    "bc_loo=nx.betweenness_centrality(G_loo_stability)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0.7272727272727273,\n",
       " 1: 0.6666666666666666,\n",
       " 2: 0.8,\n",
       " 3: 0.8888888888888888,\n",
       " 4: 0.8,\n",
       " 5: 0.6666666666666666,\n",
       " 6: 0.8,\n",
       " 7: 0.7272727272727273,\n",
       " 8: 0.8888888888888888}"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_5fold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0.023809523809523808,\n",
       " 1: 0.013095238095238094,\n",
       " 2: 0.04285714285714285,\n",
       " 3: 0.1125,\n",
       " 4: 0.04583333333333332,\n",
       " 5: 0.007142857142857143,\n",
       " 6: 0.05595238095238095,\n",
       " 7: 0.026785714285714284,\n",
       " 8: 0.06488095238095237}"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bc_5fold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0.7272727272727273,\n",
       " 1: 0.6666666666666666,\n",
       " 2: 0.6666666666666666,\n",
       " 3: 0.8,\n",
       " 4: 0.5714285714285714,\n",
       " 5: 0.6666666666666666,\n",
       " 6: 0.5714285714285714,\n",
       " 7: 0.5714285714285714,\n",
       " 8: 0.7272727272727273}"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_10fold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Betweeness centrality 5-fold')"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(bc_5fold.values())\n",
    "plt.title('Betweeness centrality 5-fold')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Closeness centrality 5-fold')"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(cc_5fold.values())\n",
    "plt.title('Closeness centrality 5-fold')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0.8,\n",
       " 1: 0.7272727272727273,\n",
       " 2: 0.7272727272727273,\n",
       " 3: 0.8888888888888888,\n",
       " 4: 0.8,\n",
       " 5: 0.6666666666666666,\n",
       " 6: 0.8,\n",
       " 7: 0.8,\n",
       " 8: 0.8888888888888888}"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_5fold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0.019047619047619046,\n",
       " 1: 0.057738095238095234,\n",
       " 2: 0.027976190476190474,\n",
       " 3: 0.1261904761904762,\n",
       " 4: 0.09345238095238093,\n",
       " 5: 0.08154761904761904,\n",
       " 6: 0.023214285714285715,\n",
       " 7: 0.01607142857142857,\n",
       " 8: 0.019047619047619046}"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bc_10fold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0.6666666666666666,\n",
       " 1: 0.7272727272727273,\n",
       " 2: 0.7272727272727273,\n",
       " 3: 0.8888888888888888,\n",
       " 4: 0.8,\n",
       " 5: 0.8,\n",
       " 6: 0.7272727272727273,\n",
       " 7: 0.6666666666666666,\n",
       " 8: 0.6666666666666666}"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_10fold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0.0,\n",
       " 1: 0.125,\n",
       " 2: 0.3988095238095238,\n",
       " 3: 0.15476190476190477,\n",
       " 4: 0.047619047619047616,\n",
       " 5: 0.10119047619047618,\n",
       " 6: 0.0,\n",
       " 7: 0.10714285714285714,\n",
       " 8: 0.10119047619047618}"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bc_loo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0.5,\n",
       " 1: 0.5333333333333333,\n",
       " 2: 0.7272727272727273,\n",
       " 3: 0.6153846153846154,\n",
       " 4: 0.47058823529411764,\n",
       " 5: 0.5333333333333333,\n",
       " 6: 0.5,\n",
       " 7: 0.5714285714285714,\n",
       " 8: 0.6153846153846154}"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_loo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Draw the graphs after stability "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import networkx as nx\n",
    "G=G_loo_stability\n",
    "#G=nx.from_numpy_matrix(final_sim_kf5)\n",
    "pos=nx.spring_layout(G) # positions for all nodes\n",
    "\n",
    "# nodes\n",
    "nx.draw_networkx_nodes(G,pos,\n",
    "                       nodelist=[0,1,2,3,4,5,6,7,8],\n",
    "                       node_color='r',\n",
    "                       node_size=500,\n",
    "                   alpha=0.8)\n",
    "#nx.draw_networkx_nodes(G,pos,\n",
    "#                       nodelist=[4,5,6,7],\n",
    "#                       node_color='b',\n",
    "#                       node_size=500,\n",
    "#                   alpha=0.8)\n",
    "\n",
    "# edges\n",
    "nx.draw_networkx_edges(G,pos,width=1.0,alpha=0.5)\n",
    "nx.draw_networkx_edges(G,pos,\n",
    "                       width=2,alpha=0.5,edge_color='b')\n",
    "#nx.draw_networkx_edges(G,pos,\n",
    "#                       edgelist=[(4,5),(5,6),(6,7),(7,4)],\n",
    "#                       width=8,alpha=0.5,edge_color='b')\n",
    "\n",
    "#reliefF,lap_score,ll_l21,UDFS,fisher_score,chi_square,gini_index\n",
    "# some math labels\n",
    "labels={}\n",
    "labels[0]=r'reliefF'\n",
    "labels[1]=r'lap_score'\n",
    "labels[2]=r'll_l21'\n",
    "labels[3]=r'ls_l21'\n",
    "labels[4]=r'UDFS'\n",
    "labels[5]=r'fisher_score'\n",
    "labels[6]=r'chi_square'\n",
    "labels[7]=r'gini_index'\n",
    "labels[8]=r'SPEC'\n",
    "nx.draw_networkx_labels(G,pos,labels,font_size=12)\n",
    "\n",
    "#plt.axis('off')\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (5,5)\n",
    "#plt.savefig(\"5foldstability_graph.png\") # save as png\n",
    "plt.title('RH: loo graph with stability')\n",
    "#plt.figure(figsize=(10,10))\n",
    "plt.show() # display"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reproducibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "def intersection(a,b):\n",
    "    ''' A function that return the percentage of common elements to both a and b'''\n",
    "    sum_=0\n",
    "    for x in range(a.shape[0]):\n",
    "       # for y in range(a.shape[1]):\n",
    "        sum_+=np.sum((a[x]==b[x]))\n",
    "    #percentage=(sum_*100)/a.size\n",
    "    percentage=(sum_)/a.size\n",
    "    return(percentage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "def FS_to_FS_similarity(FS_k,k):\n",
    "    n=len(FS_k)\n",
    "    m=np.ones((n,n))\n",
    "    for i in range(n):\n",
    "        for j in range(i+1,n):\n",
    "            m[i,j]=intersection(FS_k[i],FS_k[j])/k\n",
    "            m[j,i]=m[i,j]\n",
    "    for i in range(n):\n",
    "        m[i,i]=intersection(FS_k[i],FS_k[i])/k#/100\n",
    "    return(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Nicholas_Reproducibility(k):\n",
    "    \n",
    "    top_k_ls_l21=[dataframe_ranking_5fold.ls_l21.loc[k],dataframe_ranking_10fold.ls_l21.loc[k],dataframe_ranking_loo.ls_l21.loc[k]]\n",
    "    #top_k_reliefF=[df_ranking_5fold.reliefF.loc[k],df_ranking_10fold.reliefF.loc[k],df_ranking_loo.reliefF.loc[k]]\n",
    "    #top_k_lap=[df_ranking_5fold.lap_score.loc[k],df_ranking_10fold.lap_score.loc[k],df_ranking_loo.lap_score.loc[k]]\n",
    "    #top_k_ll=[df_ranking_5fold.ll_l21.loc[k],df_ranking_10fold.ll_l21.loc[k],df_ranking_loo.ll_l21.loc[k]]\n",
    "    mat_ls_l21=FS_to_FS_similarity(top_k_ls_l21,k)\n",
    "    #mat_reliefF=FS_to_FS_similarity(top_k_reliefF,k)\n",
    "    #mat_lap=FS_to_FS_similarity(top_k_lap,k)\n",
    "    #mat_ll=FS_to_FS_similarity(top_k_ll,k)\n",
    "    #avg_mat= (mat_reliefF+mat_lap+mat_ll)/3\n",
    "    labels=['5fold','10fold','LOO']\n",
    "    dataframe_ls_l21=pd.DataFrame(mat_ls_l21,columns=labels,index=labels)\n",
    "    #dataframe_lap=pd.DataFrame(mat_lap,columns=labels,index=labels)\n",
    "    #dataframe_ll=pd.DataFrame(mat_ll,columns=labels,index=labels)\n",
    "    #dataframe_avg=pd.DataFrame(avg_mat,columns=labels,index=labels)\n",
    "    #return(dataframe_reliefF,dataframe_lap,dataframe_ll,dataframe_avg)\n",
    "    return(dataframe_ls_l21)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ls_l21_reproducibility=Nicholas_Reproducibility(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "Average_Nicholas_reproducibility=(np.average(np.triu(df_ls_l21_reproducibility.values,k=1)))*(10**3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.333333333333333"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Average_Nicholas_reproducibility"
   ]
  },
  {
   "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": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}