AHP University choice.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "prostate-carol",
   "metadata": {},
   "source": [
    "### <u>University choice with Analytical Hierarchy Process (AHP)</u>\n",
    "#### By Kashosi Aser\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "saving-think",
   "metadata": {},
   "source": [
    "#### Task\n",
    "\n",
    "During this project, I will be using AHP to determine which university I should choose for my masters based on my personal preferences.\n",
    "The number of alternatives is 4. The names of the universities will remain anonymous for obvious reasons.\n",
    "I will replace the names by A1, A2, A3 and A4.\n",
    "\n",
    "| Description of universities | Name |\n",
    "| --- | --- |\n",
    "| <b>University 1</b>: This University Has a world ranking not above of my low-rank standards. No prospect of academic success for the students (I have not read the articles published by their foreign students). No apparent post-graduation placement on the job market. The language barrier of the host country is moderate. The host country is not that exciting. The cultural barrier can however be easily crossed. | <b>A1</b> |\n",
    "| <b>University 2</b>: The Ranking is better. Promising academic prospects, even for foreigns (1 in 7 being foreign students). The acceptance rate is 29%. Welcoming with good academic development. The country of origin has a lot to offer. The cultural barrier can be crossed. | <b>A2</b> |\n",
    "| <b>University 3</b>: World Ranking is fair. The chances of placement in the labor market seem good and the country is pleasant. However, crossing language and cultural barriers is a must,and that will not be easy. | <b>A3</b> |\n",
    "| <b>University 4</b>: The Ranking is more than fair, Scholarship. This university turns out to have good programs (one needs to publish at least 2 papers to complete the first year). Chances of getting admission are 0-3%. I despise the host country. The linguistic and cultural barrier is almost impossible to cross. | <b>A4</b> |"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "seven-airplane",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "declared-coating",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "invalid-sheffield",
   "metadata": {},
   "source": [
    "#### Establish the criteria\n",
    "I will first describe the criteria on which my decision will be based \n",
    "\n",
    "\n",
    "|   | criteria   |\n",
    "| --- | ------ |\n",
    "| 1 |Global Ranking |\n",
    "| 2 |Tuition fees |\n",
    "| 3 |Academic recognition |\n",
    "| 4 |Acceptancy rate |\n",
    "| 5 |Accommodation |\n",
    "| 6 |Job Placement |\n",
    "| 7 |Country |\n",
    "| 8 |Language Barrier |"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "systematic-accessory",
   "metadata": {},
   "source": [
    "#### 1. Alternatives compared with respect to Global Ranking\n",
    "\n",
    "<table>\n",
    "    <thead>\n",
    "        <th>Global Ranking<th>\n",
    "        <th>A1<th>\n",
    "        <th>A2<th>\n",
    "        <th>A3<th>\n",
    "        <th>A4<th>\n",
    "    </thead>\n",
    "    <tbody>\n",
    "        <tr>\n",
    "            <th>A1</th>\n",
    "            <td colspan=\"2\">1</td>\n",
    "            <td colspan=\"2\">1/8</td>\n",
    "            <td colspan=\"2\">1/3</td>\n",
    "            <td colspan=\"2\">1/7</td>\n",
    "        </tr>\n",
    "        <tr>\n",
    "            <th>A2</th>\n",
    "            <td colspan=\"2\">8</td>\n",
    "            <td colspan=\"2\">1</td>\n",
    "            <td colspan=\"2\">6</td>\n",
    "            <td colspan=\"2\">5</td>\n",
    "        </tr>\n",
    "        <tr>\n",
    "            <th>A3</th>\n",
    "            <td colspan=\"2\">3</td>\n",
    "            <td colspan=\"2\">1/6</td>\n",
    "            <td colspan=\"2\">1</td>\n",
    "            <td colspan=\"2\">1/2</td>\n",
    "        </tr>\n",
    "        <tr>\n",
    "            <th>A4</th>\n",
    "            <td colspan=\"2\">7</td>\n",
    "            <td colspan=\"2\">1/5</td>\n",
    "            <td colspan=\"2\">2</td>\n",
    "            <td colspan=\"2\">1</td>\n",
    "        </tr>\n",
    "    </tbody>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "governing-banner",
   "metadata": {},
   "source": [
    "I will first add two variables that will do the following(for lack of imagination, I will use the same name used in most AHP tutorials <strong>\"M_LOCAL\"</strong> and <strong>\"y_GOAL\"</strong>)\n",
    "<ul>\n",
    "    <li>M_LOCAL : A matrix that will hold all local priority vectors of alternatives</li>\n",
    "    <li>y_GOAL: The priority vector for our final pairwise matrix</li>\n",
    "</ul>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "narrative-controversy",
   "metadata": {},
   "outputs": [],
   "source": [
    "M_LOCAL = []\n",
    "y_GOAL = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "activated-modem",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.125     , 0.33333333, 0.14285714],\n",
       "       [8.        , 1.        , 6.        , 5.        ],\n",
       "       [3.        , 0.16666667, 1.        , 0.5       ],\n",
       "       [7.        , 0.2       , 2.        , 1.        ]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pairwise comparison matrix with respect to global Ranking\n",
    "g_ranking_matrix = np.array([[1, 1/8, 1/3, 1/7],\n",
    "                         [8, 1, 6, 5],\n",
    "                         [3, 1/6, 1, 1/2],\n",
    "                         [7, 1/5, 2, 1]])    \n",
    "\n",
    "g_ranking_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "preliminary-lobby",
   "metadata": {},
   "source": [
    "### prior_vec()\n",
    "\n",
    "Function for calculating the priority vector and consistency index with given pairwise comparison matrix A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "stable-bachelor",
   "metadata": {},
   "outputs": [],
   "source": [
    " def prior_vec(A):\n",
    "    \n",
    "    # L - eigenvalues, v - eigenvectors\n",
    "    L, v = np.linalg.eig(A)\n",
    "    \n",
    "    # eigenvalues can be complex, we calculate absolute values\n",
    "    abs_L = np.abs(L)\n",
    "    \n",
    "    # max eigenvalue\n",
    "    lambda_max = max(abs_L)\n",
    "\n",
    "    # eigenvector corresponding to the max eigenvalue\n",
    "    i = np.argmax(abs_L)\n",
    "    W = v[:, i]\n",
    "    w = W/sum(W)\n",
    "    \n",
    "    # consistency index\n",
    "    n = A.shape[0]\n",
    "    cons_index = (lambda_max - n)/(n - 1)\n",
    "       \n",
    "    return cons_index, np.abs(w)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "existing-violence",
   "metadata": {},
   "source": [
    "### get_result() \n",
    "\n",
    "Function for generating the final results given a pairwise comparison matrix M, and/or labels, degree of the axis to accommodate labels \n",
    "The \"computeGlobal\" parameter helps differentiate between local and gobal results.\n",
    "We really don't want to append the global results to the locals do we :)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "necessary-sally",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_result(M, labels=[], computeGlobal=False ,dgrLabels=0):\n",
    "    # CI - consistency index, PV - priority vector\n",
    "    CI, PV = prior_vec(M)\n",
    "    \n",
    "    # add the priority vector to our M_LOCAL for local results only otherwise update y_GOAL(for global only)\n",
    "    if computeGlobal:y_GOAL.append(PV)\n",
    "    else: M_LOCAL.append(PV)\n",
    "   \n",
    "    \n",
    "    # Random consistency indexes\n",
    "    RI = np.array([\n",
    "        [1, 0],\n",
    "        [2, 0],\n",
    "        [3, 0.52],\n",
    "        [4, 0.89],\n",
    "        [5, 1.11],\n",
    "        [6, 1.25],\n",
    "        [7, 1.35],\n",
    "        [8, 1.40],\n",
    "        [9, 1.45],\n",
    "        [10, 1.49],\n",
    "    ])\n",
    "    # get size of matrix M\n",
    "    # check if M is a square Matrix\n",
    "    if len(M.shape) < 3 and M.shape[0] == M.shape[1]: size = M.shape[0]\n",
    "    else : return \"wrong input\"\n",
    "    \n",
    "    # CR - consistency ratio = CI/RI\n",
    "    CR = 0\n",
    "    for ele in RI:\n",
    "        if ele[0] == size:\n",
    "            # CR - consistency ratio = CI/RI (see above)\n",
    "            CR = CI/ele[1]\n",
    "            \n",
    "    if CR < 0.1:\n",
    "        plt.bar(labels, PV)\n",
    "        plt.xticks(rotation = dgrLabels) # Rotates X-Axis Ticks by DgrLabels-degrees\n",
    "        plt.show()\n",
    "        print(f'priority vector {PV.round(4)}')\n",
    "        print(f'consistency index {CI.round(4)}')\n",
    "        print(f'consistency ratio {CR.round(4)}')\n",
    "    else : return \"The matrix is inconsistent\"\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "technological-course",
   "metadata": {},
   "source": [
    "#### Global ranking results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "smooth-complex",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "priority vector [0.0455 0.6394 0.1082 0.2069]\n",
      "consistency index 0.0673\n",
      "consistency ratio 0.0756\n"
     ]
    }
   ],
   "source": [
    "get_result(M=g_ranking_matrix, labels=[\"A1\",\"A2\",\"A3\",\"A4\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "needed-trial",
   "metadata": {},
   "source": [
    "#### 2. Aternatives compared with respect to Tuition fees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "utility-bankruptcy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 3.        , 1.        , 0.14285714],\n",
       "       [0.33333333, 1.        , 0.33333333, 0.125     ],\n",
       "       [1.        , 3.        , 1.        , 0.14285714],\n",
       "       [7.        , 8.        , 7.        , 1.        ]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pairwise comparison matrix with respect to tuition fees\n",
    "tuition_matrix = np.array([[1, 3, 1, 1/7],\n",
    "                         [1/3, 1, 1/3, 1/8],\n",
    "                         [1, 3, 1, 1/7],\n",
    "                         [7, 8, 7, 1]])    \n",
    "\n",
    "tuition_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "random-account",
   "metadata": {},
   "source": [
    "#### Tuition results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "sustainable-prompt",
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "priority vector [0.1242 0.0545 0.1242 0.6972]\n",
      "consistency index 0.0395\n",
      "consistency ratio 0.0444\n"
     ]
    }
   ],
   "source": [
    "get_result(M=tuition_matrix, labels=[\"A1\",\"A2\",\"A3\",\"A4\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "exempt-least",
   "metadata": {},
   "source": [
    "#### 3. Aternatives compared with respect to Academic recognition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "separate-integer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.2       , 0.33333333, 0.16666667],\n",
       "       [5.        , 1.        , 4.        , 0.33333333],\n",
       "       [3.        , 0.25      , 1.        , 0.2       ],\n",
       "       [6.        , 3.        , 5.        , 1.        ]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pairwise comparison matrix with respect to Academic recognition\n",
    "acad_matrix = np.array([[1, 1/5, 1/3, 1/6],\n",
    "                         [5, 1, 4, 1/3],\n",
    "                         [3, 1/4, 1, 1/5],\n",
    "                         [6, 3, 5, 1]])    \n",
    "\n",
    "acad_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "behind-formula",
   "metadata": {},
   "source": [
    "#### Academic achievements results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "unusual-margin",
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "priority vector [0.0577 0.2848 0.1104 0.5471]\n",
      "consistency index 0.068\n",
      "consistency ratio 0.0765\n"
     ]
    }
   ],
   "source": [
    "get_result(M=acad_matrix, labels=[\"A1\",\"A2\",\"A3\",\"A4\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "perfect-mustang",
   "metadata": {},
   "source": [
    "#### 4. Alternatives compared with respect to Acceptancy rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "finished-techno",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 7.        , 3.        , 8.        ],\n",
       "       [0.14285714, 1.        , 0.25      , 2.        ],\n",
       "       [0.33333333, 4.        , 1.        , 6.        ],\n",
       "       [0.125     , 0.5       , 0.16666667, 1.        ]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pairwise comparison matrix with respect to Acceptancy rate\n",
    "accept_r_matrix = np.array([[1, 7, 3, 8],\n",
    "                         [1/7, 1, 1/4, 2],\n",
    "                         [1/3, 4, 1, 6],\n",
    "                         [1/8, 1/2, 1/6, 1]])    \n",
    "\n",
    "accept_r_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "durable-rings",
   "metadata": {},
   "source": [
    "#### Acceptancy rate results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "mechanical-coast",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8+yak3AAAACXBIWXMAAAsTAAALEwEAmpwYAAANxklEQVR4nO3dcWyc913H8feniTIhWhgQM40k1BHLhAJUhZkMBIwKOkhUlExagUQCWjSIJoiYVIZIBcofmYTaFQZ/EKQGqNRVqrKuE8hQs4AGgw3RKu4onZIqnckKcUCq15UBmmgW+uUPX8vhnH1PkrMv/vF+SVHueZ6ffN/+ZL97ufPZqSokSevfDeMeQJI0GgZdkhph0CWpEQZdkhph0CWpERvHdcebN2+uycnJcd29JK1LTz/99BeqamLQtbEFfXJyktnZ2XHdvSStS0n+ablrPuUiSY0w6JLUiE5BT7I7ydkkc0kOL7PmJ5KcSXI6yaOjHVOSNMzQ59CTbACOAe8E5oFTSaar6kzfmh3AvcD3VdXLSb5xtQaWJA3W5RH6LmCuqs5V1UXgBLBvyZqfB45V1csAVfXiaMeUJA3TJehbgPN9x/O9c/3eCrw1yd8meTLJ7kEfKMnBJLNJZhcWFq5uYknSQKN6UXQjsAO4DTgA/H6SNy5dVFXHq2qqqqYmJgZ+G6Uk6Sp1CfoFYFvf8dbeuX7zwHRVfaWqPg88z2LgJUlrpEvQTwE7kmxPsgnYD0wvWfPHLD46J8lmFp+COTe6MSVJwwz9LpequpTkEHAS2AA8VFWnkxwFZqtqunftR5KcAf4b+JWqemm1hp48/MRqfeh14YX77hj3CJKuQ53e+l9VM8DMknNH+m4XcE/vjyRpDHynqCQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiM6BT3J7iRnk8wlOTzg+t1JFpI80/vzc6MfVZK0ko3DFiTZABwD3gnMA6eSTFfVmSVLP1JVh1ZhRklSB10eoe8C5qrqXFVdBE4A+1Z3LEnSleoS9C3A+b7j+d65pd6d5NkkjyfZNpLpJEmdjepF0T8BJqvqFuAvgIcHLUpyMMlsktmFhYUR3bUkCboF/QLQ/4h7a+/c66rqpap6pXf4B8DbBn2gqjpeVVNVNTUxMXE180qSltEl6KeAHUm2J9kE7Aem+xckeXPf4V7gudGNKEnqYuh3uVTVpSSHgJPABuChqjqd5CgwW1XTwC8l2QtcAr4I3L2KM0uSBhgadICqmgFmlpw70nf7XuDe0Y4mSboSvlNUkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEZ2CnmR3krNJ5pIcXmHdu5NUkqnRjShJ6mJo0JNsAI4Be4CdwIEkOwesuwl4H/DUqIeUJA3X5RH6LmCuqs5V1UXgBLBvwLoPAPcD/zXC+SRJHXUJ+hbgfN/xfO/c65J8F7Ctqp4Y4WySpCtwzS+KJrkB+BDwyx3WHkwym2R2YWHhWu9aktSnS9AvANv6jrf2zr3mJuDbgU8meQH4HmB60AujVXW8qqaqampiYuLqp5YkXaZL0E8BO5JsT7IJ2A9Mv3axqr5UVZurarKqJoEngb1VNbsqE0uSBhoa9Kq6BBwCTgLPAY9V1ekkR5PsXe0BJUndbOyyqKpmgJkl544ss/a2ax9LknSlfKeoJDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIwy6JDXCoEtSIzoFPcnuJGeTzCU5POD6e5N8NskzST6dZOfoR5UkrWRo0JNsAI4Be4CdwIEBwX60qr6jqm4FPgh8aNSDSpJW1uUR+i5grqrOVdVF4ASwr39BVf173+FXAzW6ESVJXWzssGYLcL7veB54+9JFSX4RuAfYBPzQSKaTJHU2shdFq+pYVX0L8KvArw9ak+RgktkkswsLC6O6a0kS3YJ+AdjWd7y1d245J4B3DbpQVceraqqqpiYmJjoPKUkarkvQTwE7kmxPsgnYD0z3L0iyo+/wDuBzoxtRktTF0OfQq+pSkkPASWAD8FBVnU5yFJitqmngUJLbga8ALwN3rebQkqTLdXlRlKqaAWaWnDvSd/t9I55LknSFfKeoJDXCoEtSIwy6JDXCoEtSIzq9KCrpf00efmLcI4zdC/fdMe4RNICP0CWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhph0CWpEQZdkhrRKehJdic5m2QuyeEB1+9JcibJs0k+keTm0Y8qSVrJ0KAn2QAcA/YAO4EDSXYuWfb3wFRV3QI8Dnxw1INKklbW5RH6LmCuqs5V1UXgBLCvf0FV/VVVfbl3+CSwdbRjSpKG6RL0LcD5vuP53rnlvAf4s0EXkhxMMptkdmFhofuUkqShRvqiaJKfAqaABwZdr6rjVTVVVVMTExOjvGtJ+n9vY4c1F4Btfcdbe+f+jyS3A78G/GBVvTKa8SRJXXV5hH4K2JFke5JNwH5gun9Bku8EHgT2VtWLox9TkjTM0KBX1SXgEHASeA54rKpOJzmaZG9v2QPAjcBHkzyTZHqZDydJWiVdnnKhqmaAmSXnjvTdvn3Ec0mSrpDvFJWkRhh0SWqEQZekRhh0SWqEQZekRhh0SWqEQZekRhh0SWqEQZekRhh0SWqEQZekRhh0SWqEQZekRhh0SWqEQZekRhh0SWqEQZekRhh0SWqEQZekRhh0SWqEQZekRhh0SWqEQZekRhh0SWqEQZekRhh0SWpEp6An2Z3kbJK5JIcHXH9Hks8kuZTkztGPKUkaZmjQk2wAjgF7gJ3AgSQ7lyz7Z+Bu4NFRDyhJ6mZjhzW7gLmqOgeQ5ASwDzjz2oKqeqF37dVVmFGS1EGXp1y2AOf7jud7565YkoNJZpPMLiwsXM2HkCQtY01fFK2q41U1VVVTExMTa3nXktS8LkG/AGzrO97aOydJuo50CfopYEeS7Uk2AfuB6dUdS5J0pYYGvaouAYeAk8BzwGNVdTrJ0SR7AZJ8d5J54MeBB5OcXs2hJUmX6/JdLlTVDDCz5NyRvtunWHwqRpI0Jp2CrrZMHn5i3COM1Qv33THuEaRV4Vv/JakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRBl2SGmHQJakRvlNU0prz3cqr825lH6FLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiMMuiQ1wqBLUiM6BT3J7iRnk8wlOTzg+huSfKR3/akkkyOfVJK0oqFBT7IBOAbsAXYCB5LsXLLsPcDLVfUW4LeB+0c9qCRpZV0eoe8C5qrqXFVdBE4A+5as2Qc83Lv9OPDDSTK6MSVJw3T5FXRbgPN9x/PA25dbU1WXknwJ+AbgC/2LkhwEDvYO/zPJ2asZ+jqwmSX/bWsp6//fP+7ftXMPr8163r+bl7uwpr9TtKqOA8fX8j5XQ5LZqpoa9xzrlft37dzDa9Pq/nV5yuUCsK3veGvv3MA1STYCXwu8NIoBJUnddAn6KWBHku1JNgH7gekla6aBu3q37wT+sqpqdGNKkoYZ+pRL7znxQ8BJYAPwUFWdTnIUmK2qaeAPgUeSzAFfZDH6LVv3TxuNmft37dzDa9Pk/sUH0pLUBt8pKkmNMOiS1AiD3kGSdyWpJN/ad+7jSf4tyZ+Oc7b1YOn+Jbk1yd8lOZ3k2SQ/Oe4Zr2cD9u/mJJ9J8kxvD9877hmvZ4O+fnvnvybJfJLfHddso2bQuzkAfLr392seAH56POOsO0v378vAz1TVtwG7gd9J8sYxzbYeLN2/fwW+t6puZfFNfoeTfNOYZlsPBn39AnwA+Ju1H2f1GPQhktwIfD+LP6/m9e/eqapPAP8xrrnWi0H7V1XPV9Xnerf/BXgRmBjbkNexZfbvYlW90lvyBvw6XtZyX79J3ga8CfjzMY22KvxEGG4f8PGqeh54qfeJoO5W3L8ku4BNwD+OY7h1YOD+JdmW5FkWf+TG/b3/Mepyl+1fkhuA3wLeP97RRs+gD3eAxR9IRu/vpf9s08qW3b8kbwYeAX62ql4dw2zrwcD9q6rzVXUL8BbgriRvGtN817tB+/cLwExVzY9tqlXi96GvIMnXs/jDyBaAYvGNVQXcXFWV5Dbg/VX1Y2Mb8jq20v4BNwGfBH6jqh4f14zXs2Gff33rHmIxUO5jnxX271PADwCvAjey+C/E36uqy37Xw3rjI/SV3Qk8UlU3V9VkVW0DPs/iJ4OGW2n//gj4sBFa0bL7l+SrAJJ8HYvPEa/Xn1y6mpbbvwer6purapLFp10+3ELMwaAPc4DF8PT7GIu/5ONTwEdZ/Nnv80l+dM2nu/4tt38PA+8A7u59690zSW5d6+HWgeX270HgqST/APw18JtV9dm1Hm4dWPbrdwyzrAmfcpGkRvgIXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqREGXZIa8T9B65nyTMyUBwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "priority vector [0.5908 0.0834 0.2736 0.0522]\n",
      "consistency index 0.0283\n",
      "consistency ratio 0.0318\n"
     ]
    }
   ],
   "source": [
    "get_result(M=accept_r_matrix, labels=[\"A1\",\"A2\",\"A3\",\"A4\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "congressional-outreach",
   "metadata": {},
   "source": [
    "#### 5. Alternatives compared with respect to Accommodation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "instant-fantasy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 5.        , 3.        , 0.33333333],\n",
       "       [0.2       , 1.        , 0.33333333, 0.16666667],\n",
       "       [0.33333333, 3.        , 1.        , 0.2       ],\n",
       "       [3.        , 6.        , 5.        , 1.        ]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pairwise comparison matrix with respect to Accommodation\n",
    "accom_matrix = np.array([[1, 5, 3, 1/3],\n",
    "                         [1/5, 1, 1/3, 1/6],\n",
    "                         [1/3, 3, 1, 1/5],\n",
    "                         [3, 6, 5, 1]])    \n",
    "\n",
    "accom_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "outdoor-photograph",
   "metadata": {},
   "source": [
    "#### Accommodation results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "wanted-intent",
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "priority vector [0.2661 0.0589 0.1195 0.5554]\n",
      "consistency index 0.0501\n",
      "consistency ratio 0.0563\n"
     ]
    }
   ],
   "source": [
    "get_result(M=accom_matrix, labels=[\"A1\",\"A2\",\"A3\",\"A4\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "surrounded-christopher",
   "metadata": {},
   "source": [
    "#### 6. Alternatives compared with respect to Job Placement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "seven-eugene",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.2       , 0.33333333, 2.        ],\n",
       "       [5.        , 1.        , 3.        , 6.        ],\n",
       "       [3.        , 0.33333333, 1.        , 5.        ],\n",
       "       [0.5       , 0.16666667, 0.2       , 1.        ]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pairwise comparison matrix with respect to Job Placement\n",
    "job_matrix = np.array([[1, 1/5, 1/3, 2],\n",
    "                         [5, 1, 3, 6],\n",
    "                         [3, 1/3, 1, 5],\n",
    "                         [1/2, 1/6, 1/5, 1]])    \n",
    "\n",
    "job_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "determined-tunisia",
   "metadata": {},
   "source": [
    "#### Job Placement results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "found-protocol",
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "priority vector [0.107  0.5594 0.2688 0.0648]\n",
      "consistency index 0.0312\n",
      "consistency ratio 0.0351\n"
     ]
    }
   ],
   "source": [
    "get_result(M=job_matrix, labels=[\"A1\",\"A2\",\"A3\",\"A4\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "copyrighted-angel",
   "metadata": {},
   "source": [
    "#### 7. Alternatives compared with respect to Country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "flush-kitty",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.25      , 0.25      , 0.33333333],\n",
       "       [4.        , 1.        , 1.        , 5.        ],\n",
       "       [4.        , 1.        , 1.        , 5.        ],\n",
       "       [3.        , 0.2       , 0.2       , 1.        ]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pairwise comparison matrix with respect to the Country\n",
    "country_matrix = np.array([[1, 1/4, 1/4, 1/3],\n",
    "                         [4, 1, 1, 5],\n",
    "                         [4, 1, 1, 5],\n",
    "                         [3, 1/5, 1/5, 1]])    \n",
    "\n",
    "country_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "verified-square",
   "metadata": {},
   "source": [
    "#### Country results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "little-nicholas",
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "priority vector [0.0748 0.4029 0.4029 0.1195]\n",
      "consistency index 0.0753\n",
      "consistency ratio 0.0846\n"
     ]
    }
   ],
   "source": [
    "get_result(M=country_matrix, labels=[\"A1\",\"A2\",\"A3\",\"A4\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acquired-somewhere",
   "metadata": {},
   "source": [
    "#### 8. Alternatives compared with respect to the Language Barrier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "mental-excerpt",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.  , 2.  , 2.  , 5.  ],\n",
       "       [0.5 , 1.  , 1.  , 4.  ],\n",
       "       [0.5 , 1.  , 1.  , 4.  ],\n",
       "       [0.2 , 0.25, 0.25, 1.  ]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pairwise comparison matrix with respect to the Language Barrier\n",
    "lg_matrix = np.array([[1, 2, 2, 5],\n",
    "                         [1/2, 1, 1, 4],\n",
    "                         [1/2, 1, 1, 4],\n",
    "                         [1/5, 1/4, 1/4, 1]])    \n",
    "\n",
    "lg_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "frequent-quantum",
   "metadata": {},
   "source": [
    "#### Language Barrier results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "composed-semiconductor",
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "priority vector [0.0748 0.4029 0.4029 0.1195]\n",
      "consistency index 0.0753\n",
      "consistency ratio 0.0846\n"
     ]
    }
   ],
   "source": [
    "get_result(M=country_matrix, labels=[\"A1\",\"A2\",\"A3\",\"A4\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "worldwide-directory",
   "metadata": {},
   "source": [
    "## Criteria comparison for the ultimate choice"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "third-fiber",
   "metadata": {},
   "source": [
    "<table>\n",
    "    <thead>\n",
    "        <th></th>\n",
    "        <th>Global Ranking</th>\n",
    "        <th>Tuition fees</th>\n",
    "        <th>Academic recognition</th>       \n",
    "        <th>Accommodation</th>\n",
    "        <th>Job Placement</th>\n",
    "        <th>Country</th>\n",
    "        <th>Language Barrier</th>\n",
    "         <th>Acceptancy rate</th>\n",
    "    </thead>\n",
    "    <tbody>\n",
    "        <tr>\n",
    "            <th>Global Ranking</th>\n",
    "            <td>1</td>\n",
    "            <td>1/3</td>\n",
    "            <td>3</td>\n",
    "            <td>5</td>\n",
    "            <td>1/3</td>\n",
    "            <td>7</td>\n",
    "            <td>6</td>\n",
    "            <td>4</td>\n",
    "        </tr>\n",
    "        <tr>\n",
    "            <th>Tuition fees</th>\n",
    "            <td >3</td>\n",
    "            <td >1</td>\n",
    "            <td >4</td>\n",
    "            <td >6</td>\n",
    "            <td >1</td>\n",
    "            <td >8</td>\n",
    "            <td>7</td>\n",
    "            <td >5</td>\n",
    "        </tr>\n",
    "        <tr>\n",
    "            <th>Academic recognition</th>\n",
    "            <td >1/3</td>\n",
    "            <td>1/4</td>\n",
    "            <td >1</td>\n",
    "            <td>4</td>\n",
    "            <td>1/4</td>\n",
    "            <td>6</td>\n",
    "            <td>5</td>\n",
    "            <td>3</td>\n",
    "        </tr>\n",
    "        <tr>\n",
    "            <th>Accommodation</th>\n",
    "            <td >1/5</td>\n",
    "            <td>1/6</td>\n",
    "            <td >1/4</td>\n",
    "            <td>1</td>\n",
    "            <td>1/6</td>\n",
    "            <td >4</td>\n",
    "            <td >3</td>\n",
    "            <td>1/3</td>\n",
    "        </tr>\n",
    "        <tr>\n",
    "            <th>Job Placement</th>\n",
    "            <td >3</td>\n",
    "            <td >1</td>\n",
    "            <td>4</td>\n",
    "            <td>6</td>\n",
    "            <td>1</td>\n",
    "            <td>8</td>\n",
    "            <td>7</td>\n",
    "            <td>5</td>\n",
    "        </tr>\n",
    "        <tr>\n",
    "            <th>Country</th>\n",
    "            <td >1/7</td>\n",
    "            <td>1/8</td>\n",
    "            <td >1/6</td>\n",
    "            <td>1/4</td>\n",
    "            <td >1/8</td>\n",
    "            <td>1</td>\n",
    "            <td >1/3</td>\n",
    "            <td >1/5</td>\n",
    "        </tr>\n",
    "        <tr>\n",
    "            <th>Language Barrier</th>\n",
    "            <td >1/6</td>\n",
    "            <td >1/7</td>\n",
    "            <td >1/5</td>\n",
    "            <td >1/3</td>\n",
    "            <td >1/7</td>\n",
    "            <td >3</td>\n",
    "            <td >1</td>\n",
    "            <td >1/4</td>\n",
    "        </tr>\n",
    "        <tr>\n",
    "            <th>Acceptancy rate</th>\n",
    "            <td>1/4</td>\n",
    "            <td >1/5</td>\n",
    "            <td >1/3</td>\n",
    "            <td >3</td>\n",
    "            <td >1/5</td>\n",
    "            <td >5</td>\n",
    "            <td >4</td>\n",
    "            <td >1</td>\n",
    "        </tr>\n",
    "    </tbody>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "lined-referral",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.33333333, 3.        , 5.        , 0.33333333,\n",
       "        7.        , 6.        , 4.        ],\n",
       "       [3.        , 1.        , 4.        , 6.        , 1.        ,\n",
       "        8.        , 7.        , 5.        ],\n",
       "       [0.33333333, 0.25      , 1.        , 4.        , 0.25      ,\n",
       "        6.        , 5.        , 3.        ],\n",
       "       [0.2       , 0.16666667, 0.25      , 1.        , 0.16666667,\n",
       "        4.        , 3.        , 0.33333333],\n",
       "       [3.        , 1.        , 4.        , 6.        , 1.        ,\n",
       "        8.        , 7.        , 5.        ],\n",
       "       [0.14285714, 0.125     , 0.16666667, 0.25      , 0.125     ,\n",
       "        1.        , 0.33333333, 0.2       ],\n",
       "       [0.16666667, 0.14285714, 0.2       , 0.33333333, 0.14285714,\n",
       "        3.        , 1.        , 0.25      ],\n",
       "       [0.25      , 0.2       , 0.33333333, 3.        , 0.2       ,\n",
       "        5.        , 4.        , 1.        ]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# global pairwise comparison matrix\n",
    "global_matrix = np.array([[1, 1/3, 3, 5, 1/3, 7, 6, 4],\n",
    "                         [3, 1, 4, 6, 1, 8, 7, 5],\n",
    "                         [1/3, 1/4, 1, 4, 1/4, 6, 5, 3],\n",
    "                         [1/5, 1/6, 1/4, 1, 1/6, 4, 3, 1/3],\n",
    "                         [3, 1, 4, 6, 1, 8, 7, 5],\n",
    "                         [1/7, 1/8, 1/6, 1/4, 1/8, 1, 1/3, 1/5],\n",
    "                         [1/6, 1/7, 1/5, 1/3, 1/7, 3, 1, 1/4],\n",
    "                         [1/4, 1/5, 1/3, 3, 1/5, 5, 4, 1]])    \n",
    "\n",
    "global_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "transparent-operation",
   "metadata": {},
   "source": [
    "## Global Criteria Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "agreed-signature",
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "priority vector [0.1692 0.2816 0.1079 0.0438 0.2816 0.019  0.0283 0.0686]\n",
      "consistency index 0.1041\n",
      "consistency ratio 0.0744\n"
     ]
    }
   ],
   "source": [
    "get_result(M=global_matrix, labels=[\n",
    "    \"Global Ranking\",\"Tuition fees\",\n",
    "    \"Academic recognition\",\n",
    "    \"Accommodation\",\n",
    "    \"Job Placement\",\n",
    "    \"Country\",\n",
    "    \"Language Barrier\",\n",
    "    \"Acceptancy rate\"],dgrLabels=90, computeGlobal=True) # and we did not forget to set our computeGlobal to True"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mounted-welding",
   "metadata": {},
   "source": [
    "## Global Priorities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "close-joseph",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.04545933, 0.12416636, 0.05771984, 0.59078053, 0.26613299,\n",
       "        0.10702601, 0.07478985, 0.07478985],\n",
       "       [0.63944043, 0.0544875 , 0.28481752, 0.08339873, 0.05892824,\n",
       "        0.55935463, 0.40285192, 0.40285192],\n",
       "       [0.10819074, 0.12416636, 0.11041152, 0.27358275, 0.11953795,\n",
       "        0.26880809, 0.40285192, 0.40285192],\n",
       "       [0.20690951, 0.69717977, 0.54705112, 0.05223799, 0.55540083,\n",
       "        0.06481126, 0.11950631, 0.11950631]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And now let's put to good use our M_LOCAL matrix\n",
    "# consisting of local priority vectors of alternatives with respect to\n",
    "# Global Ranking, Tuition fees, Academic recognition, \n",
    "# Acceptancy rate, Accommodation, Job Placement, Country, Language Barrier\n",
    "\n",
    "M_LOCAL = np.vstack (M_LOCAL).T\n",
    "\n",
    "M_LOCAL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "elementary-stick",
   "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"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global priority vector [0.159  0.2242 0.155  0.4619]\n"
     ]
    }
   ],
   "source": [
    "# global priority vector\n",
    "G = M_LOCAL.dot(np.array(y_GOAL).flatten())\n",
    "# visualization\n",
    "labels = ['A1', 'A2', 'A3', 'A4']\n",
    "plt.bar(labels, G)\n",
    "plt.show()\n",
    "print(f'Global priority vector {G.round(4)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "numerous-treasurer",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "collective-tablet",
   "metadata": {},
   "source": [
    "I would like to express how surprised I was with the results. As the graph shows, the best decision right now is to go with A4 (probably because it is a scholarship program): with a priority of 0.4619. A4 is followed by A2 (where I didn't even consider applying for) with the priority of 0.2242. A1 and A3 can feel tight, with A1 having the slightly higher priority of 0.159 (again, I wasn't even considering applying for this) versus A3 with a priority of 0.159."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
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 },
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