results.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Drug Repositioning Predictions by Non-Negative Matrix Tri-Factorization of Integrated Association Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook aims at computing and showing results obtained recently.\n",
    "They have been more precisely explained in a recently-submitted paper.\n",
    "\n",
    "This notebook contains a lot of information and work and tries to sum it up. After some preliminary work (transforming the data to fit the method we use), we describe the different optimizations made on the method and then we show the main results.\n",
    "\n",
    "This notebook is divided into 7 sections:\n",
    "1. Data and Association Matrices\n",
    "2. Mask Matrix\n",
    "3. Initialization\n",
    "4. Hyperparameters\n",
    "5. Stop Criterion\n",
    "6. Improvements\n",
    "7. Final Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#First we load the packages we need\n",
    "import sklearn.metrics as metrics\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm_notebook\n",
    "from scipy import sparse\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import csv\n",
    "\n",
    "#These two classes are implemented in the repository\n",
    "from load_data_NMTF import loader\n",
    "from method_NMTF import NMTF\n",
    "\n",
    "#While using a server to run this notebook, it can be necessary to limit the number of threads\n",
    "import os\n",
    "os.environ[\"MKL_NUM_THREADS\"] = \"5\"\n",
    "os.environ[\"NUMEXPR_NUM_THREADS\"] = \"5\"\n",
    "os.environ[\"OMP_NUM_THREADS\"] = \"5\"\n",
    "os.environ[\"OPENBLAS_NUM_THREADS\"] = \"5\"\n",
    "os.environ[\"VECLIB_MAXIMUM_THREADS\"] = \"5\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Data and Association Matrices\n",
    "\n",
    "The data is provided in different heterogeneous files. We first have to load these different files to create and save the matrices we need and that include all the data.\n",
    "\n",
    "Initially, the data is stored in heterogeneous files (in the _data_ folder):\n",
    "- **_Drugs.txt_** lists all drug IDs;\n",
    "- **_LabelsDrugs.txt_** lists all links between labels (indications) and drugs, providing label names, drug names and drug IDs;\n",
    "- **_Proteins.txt_** lists all protein IDs;\n",
    "- **_Pathways.txt_** lists all pathway IDs;\n",
    "- **_network.txt_** lists all inter-associations between drugs and proteins, and proteins and pathways;\n",
    "- **_ProteinsToProteins.txt_** lists all intra-links among proteins;\n",
    "- **_PathwaysToPathways.txt_** lists all intra-links among pathways;\n",
    "- **_DrugDisease.csv_** lists all inter-associations between drugs and diseases, giving drugs IDs and disease names.\n",
    "\n",
    "Thanks to these files, we want to get the following matrices:\n",
    "- __$R_{12}$__ : inter-associations between __labels__ and __drugs__;\n",
    "- __$R_{23}$__ : inter-associations between __drugs__ and __proteins__;\n",
    "- __$R_{34}$__ : inter-associations between __proteins__ and __pathways__;\n",
    "- __$R_{25}$__ : inter-associations between __drugs__ and __diseases__;\n",
    "- __$W_3$__ : intra-associations among __proteins__;\n",
    "- __$W_4$__ : intra-associations among __pathways__.\n",
    "\n",
    "To do so, we use the class implemented in ***load_data_NMTF.py***:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "f_labelsdrugs = 'DrugsToLabels.txt'\n",
    "f_drugsproteins = 'DrugsToProteins.txt'\n",
    "f_proteinspathways = 'ProteinsToPathways.txt'\n",
    "f_drugsdiseases = 'DrugsToDiseases.txt'\n",
    "f_protprot = 'ProteinsToProteins.txt'\n",
    "f_pathpath = 'PathwaysToPathways.txt'\n",
    "\n",
    "load = loader(f_labelsdrugs, \n",
    "              f_drugsproteins, \n",
    "              f_proteinspathways, \n",
    "              f_drugsdiseases, \n",
    "              f_protprot, \n",
    "              f_pathpath)\n",
    "\n",
    "R12, R23, R34, R25, W3, W4 = load.association_matrices()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also already compute $L_3$ and $L_4$, the laplacian matrices associated with $W_3$ and $W_4$ respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "d3 = np.array(W3.sum(axis=0))\n",
    "D3 = sparse.diags(d3[0], 0)\n",
    "L3 = D3 - W3 #laplacian matrix of intra-protein links\n",
    "\n",
    "d4 = np.array(W4.sum(axis=0))\n",
    "D4 = sparse.diags(d4[0], 0)\n",
    "L4 = D4 - W4 #laplacian matrix of intra-pathway links"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And save the results in different files as sparse matrices (which needs way less space than numpy matrices):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sparse.save_npz('./tmp/R12.npz', R12)\n",
    "#sparse.save_npz('./tmp/R23.npz', R23)\n",
    "#sparse.save_npz('./tmp/R34.npz', R34)\n",
    "#sparse.save_npz('./tmp/R25.npz', R25)\n",
    "#sparse.save_npz('./tmp/W3.npz', W3)\n",
    "#sparse.save_npz('./tmp/W4.npz', W4)\n",
    "#sparse.save_npz('./tmp/L3.npz', L3)\n",
    "#sparse.save_npz('./tmp/L4.npz', L4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From now, as we are about to focus on link prediction between labels and drugs, i.e. in $R_{12}$, it is interesting to habe a good understanding of this matrix. The number of drugs associated to one label vary a lot.\n",
    "While 1 drug is associated to _Increased IgG Production_, 666 drugs are associated to the label _Cytochrome P-450 CYP3A Substrates_. This variance can be better understood thanks to the following plot, where the diseases are ranked according to their degree:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar = np.sum(R12.toarray(), axis=1)\n",
    "bar.sort()\n",
    "X = np.arange(len(bar))\n",
    "plt.rcParams[\"figure.figsize\"] = (30,10)\n",
    "plt.bar(X, bar)\n",
    "plt.xlabel('Diseases')\n",
    "plt.ylabel('Number of associated Drugs')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, if we want some information about the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 141 labels, 3261 drugs, 3691 proteins, 1914 pathways and 841 diseases\n",
      "There are 23517 links between labels and drugs\n",
      "There are 13433 links between drugs and proteins\n",
      "There are 28345 links between proteins and pathways\n",
      "There are 2417 links between drugs and diseases\n"
     ]
    }
   ],
   "source": [
    "inter_ld = np.count_nonzero(R12.toarray())\n",
    "inter_dp = np.count_nonzero(R23.toarray())\n",
    "inter_pp = np.count_nonzero(R34.toarray())\n",
    "inter_dd = np.count_nonzero(R25.toarray())\n",
    "print('There are {} labels, {} drugs, {} proteins, {} pathways and {} diseases'.format(R12.shape[0], R12.shape[1], R23.shape[1], R34.shape[1], R25.shape[1]))\n",
    "print('There are {} links between labels and drugs'.format(inter_ld))\n",
    "print('There are {} links between drugs and proteins'.format(inter_dp))\n",
    "print('There are {} links between proteins and pathways'.format(inter_pp))\n",
    "print('There are {} links between drugs and diseases'.format(inter_dd))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Mask Matrix\n",
    "\n",
    "This step consists in creating a validation set to test our algorithm.\n",
    "For the sake of simplicity, we will first create a random matrix $M_{10}$ of the same size of $R_{12}$ which contains 10% of null elements and 90% of zeros. The indexes of null elements in this matrix will correspond to the elements of the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#M10 = np.random.binomial(1, 0.9, size=R12.shape)\n",
    "#np.save('./tmp/M10', M10)\n",
    "M10 = np.load('./tmp/M10.npy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, to make things easier, we will use this $M_{10}$ matrix.\n",
    "\n",
    "Nevertheless, to be more coherent, we could design the validation set a bit differently. Indeed, our problem is to find new links between drugs and labels, given that drugs are already associated to some labels. Then, we should build our validation set so that **every drug in the *training set* is still associated with at least one label**. To create a mask having the same proportion of positive and negatives instances as $M_{10}$ and respecting this rule, we can do as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import floor\n",
    "\n",
    "R12 = sparse.load_npz('./tmp/R12.npz').toarray()\n",
    "M10_2 = np.ones(R12.shape)\n",
    "n, m = R12.shape\n",
    "#We compute manually the number of positive and negative items of our validation set\n",
    "pos = floor(0.1 * np.count_nonzero(R12)) #2351\n",
    "neg = floor(0.1 * (n*m - np.count_nonzero(R12))) #43628\n",
    "\n",
    "p2, n2 = 0, 0\n",
    "\n",
    "def islinked(R):\n",
    "    return np.min(np.max(R, axis = 0)) == 1\n",
    "\n",
    "perm = np.random.permutation(n*m)\n",
    "index = 0\n",
    "\n",
    "while p2 < pos:\n",
    "    i = perm[index] // m\n",
    "    j = perm[index] % m\n",
    "\n",
    "    if R12[i, j] == 1 and M10_2[i, j] == 1:\n",
    "        M10_2[i, j] = 0\n",
    "        R12_res = np.multiply(M10_2, R12)\n",
    "        if islinked(R12_res):\n",
    "            p2 += 1\n",
    "        else:\n",
    "            M10_2[i, j] = 1\n",
    "    index += 1\n",
    "\n",
    "index = 0\n",
    "while n2 < neg:\n",
    "    i = perm[index] // m\n",
    "    j = perm[index] % m\n",
    "    \n",
    "    if R12[i, j] == 0 and M10_2[i, j] == 1:\n",
    "        M10_2[i, j] = 0\n",
    "        n2 += 1\n",
    "    index += 1\n",
    "\n",
    "#np.save('./tmp/M10_2', M10_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#if needed\n",
    "M10_2 = np.load('./tmp/M10_2.npy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Initialization\n",
    "\n",
    "Now that the data and the validation set are imported and created in a good format, we can start tuning our NMTF model for drug repositioning.\n",
    "The first step, as defined in the paper, is to study the initialization of the NMTF algorithm. In this section, we compute and compare the NMTF algorithm for 4 different initializations.\n",
    "- **Random initialization**\n",
    "- **Random ACOL initialization**\n",
    "- **K-means initialization**\n",
    "- **Spherical k-means initialization**\n",
    "\n",
    "We run the script _initialization.py_ to compute the APS score over 500 iterations for those initializations for a given set of hyperparameters. Then, we save those files and reload them here to plot the following curve.\n",
    "We compute 5 models for each initialization method to also see the variance of those methods.\n",
    "\n",
    "In this situation, we naively choose the parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "INIT = ['random', 'acol', 'kmeans', 'skmeans']\n",
    "plt.rcParams[\"figure.figsize\"] = (15,8)\n",
    "\n",
    "for init in INIT:\n",
    "    [loss, aps] = np.load('./tmp/initialization_' + init + '.npy')\n",
    "    X = np.arange(1, 500, 10)\n",
    "    df = pd.DataFrame(aps, columns = X).melt()\n",
    "    sns.lineplot(x=\"variable\", y=\"value\", data=df, ci='sd', label = init)\n",
    "\n",
    "plt.xlabel('Iterations')\n",
    "plt.ylabel('Average Precision Score (APS)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Hyperparameters\n",
    "\n",
    "By hyperparameters we denote $K = (k_1, k_2, k_3, k_4, k_5) \\in N^5$.\n",
    "We decided to compute the __dispersion coefficient__ as explained in the paper.\n",
    "\n",
    "Important note: the __dispersion coefficient__ is computed to assess the stability of the results, ie the matrices $R_{12}, R_{23}, R_{34}$ and $R_{25}$.\n",
    "\n",
    "For instance, as we will see in this example, we will compute $\\rho_4$, the dispersion coefficient for $k_4$ on the matrix $R_{34}$ computed by the algorithm. For this application, we will stop the algorithm after 10 iterations, and run 5 times the NMTF algorithm for a given set of parameters.\n",
    "The script _dispersion4.py_ is run and the results are reloaded here to plot the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rho4 = np.load('./tmp/rho4.npy')\n",
    "plt.rcParams[\"figure.figsize\"] = (7,5)\n",
    "X = np.arange(10, len(rho4)*20 + 1, 20)\n",
    "\n",
    "plt.plot(X, rho4, color = 'r', linewidth = 2)\n",
    "plt.axvline(500, linestyle = ':', color = 'black')\n",
    "plt.xticks([0, 200, 400, 500, 600, 800, 1000])\n",
    "plt.xlabel('k4')\n",
    "plt.ylabel('$\\\\rho$4')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this plot, $(k_1, k_2, k_3, k_5) = (500, 141, 500, 300)$.\n",
    "\n",
    "As we can see here, we decided to choose $k_4 = 500$ as a good compromise between the reproductibility of the results and the computation complexity. Finally, we decide to set $ K = (k_1, k_2, k_3, k_4, k_5) = (500, 141, 500, 500, 300)$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Stop criterion\n",
    "\n",
    "Last but not least, we had to find a good stop criterion for our algorithm. Here are the options:\n",
    "- Stop after a defined and fixed number of iterations;\n",
    "- Use a stop criterion based on the loss.\n",
    "\n",
    "As explained in the paper, we decide to use the following stop criterion:\n",
    "\n",
    "$$\\frac{J(\\textbf{G}^{(n)}) - J(\\textbf{G}^{(n+1)})}{J(\\textbf{G}^{(n+1)})} < \\epsilon$$\n",
    "\n",
    "For $\\epsilon = 0.02$ we have the following results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d2badc50c23045869afef8ce29982985",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=30), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model NMTF with (k1, k2, k3, k4, k5)=(500, 141, 500, 500, 300) and skmeans initialization\n"
     ]
    }
   ],
   "source": [
    "M10 = np.load('./tmp/M10.npy')\n",
    "\n",
    "epsilon = 2e-2\n",
    "K = [500, 141, 500, 500, 300]\n",
    "max_iter = 30\n",
    "nmtf = NMTF('skmeans', K, M10)\n",
    "I = max_iter\n",
    "    \n",
    "loss, aps = [], []\n",
    "nmtf.initialize()\n",
    "loss.append(nmtf.loss())\n",
    "for p in tqdm_notebook(range(max_iter)):\n",
    "    nmtf.iterate()\n",
    "    loss.append(nmtf.loss())\n",
    "    aps.append(nmtf.validate())\n",
    "    \n",
    "            \n",
    "    CRIT = abs((loss[-1] - loss[-2]) / loss[-1])  \n",
    "      \n",
    "    if CRIT < epsilon:\n",
    "        I = min(I, p + 1)\n",
    "\n",
    "print(nmtf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = np.arange(1, len(loss))         \n",
    "plt.rcParams[\"figure.figsize\"] = (7,5)\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "color = 'tab:red'\n",
    "ax1.set_xlabel('Iterations')\n",
    "ax1.set_ylabel('Loss', color=color)\n",
    "ax1.plot(X, loss[1:], color=color)\n",
    "ax1.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n",
    "color = 'tab:blue'\n",
    "ax2.set_ylabel('Average Precision Score', color=color)  # we already handled the x-label with ax1\n",
    "ax2.plot(X, aps, color=color)\n",
    "ax2.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "fig.tight_layout()  # otherwise the right y-label is slightly clipped\n",
    "plt.axvline(x=I, color='k', linestyle = ':')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This criterion is particularly relevant because it works with really different sets of parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "965a4b4b510149289022e75eb9e86f2a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=50), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model NMTF with (k1, k2, k3, k4, k5)=(100, 100, 100, 100, 300) and skmeans initialization\n"
     ]
    }
   ],
   "source": [
    "M10 = np.load('./tmp/M10.npy')\n",
    "\n",
    "epsilon = 2e-2\n",
    "K = [100, 100, 100, 100, 300]\n",
    "max_iter = 50\n",
    "nmtf2 = NMTF('skmeans', K, M10)\n",
    "I2 = max_iter\n",
    "    \n",
    "loss2, aps2 = [], []\n",
    "nmtf2.initialize()\n",
    "loss2.append(nmtf2.loss())\n",
    "for p in tqdm_notebook(range(max_iter)):\n",
    "    nmtf2.iterate()\n",
    "    loss2.append(nmtf2.loss())\n",
    "    aps2.append(nmtf2.validate())\n",
    "    \n",
    "            \n",
    "    CRIT = abs((loss2[-1] - loss2[-2]) / loss2[-1])  \n",
    "      \n",
    "    if CRIT < epsilon:\n",
    "        I2 = min(I2, p + 1)\n",
    "\n",
    "print(nmtf2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X2 = np.arange(1, len(loss2))         \n",
    "fig, ax1 = plt.subplots()\n",
    "color = 'tab:red'\n",
    "ax1.set_xlabel('Iterations')\n",
    "ax1.set_ylabel('Loss', color=color)\n",
    "ax1.plot(X2, loss2[1:], color=color)\n",
    "ax1.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n",
    "color = 'tab:blue'\n",
    "ax2.set_ylabel('Average Precision Score', color=color)  # we already handled the x-label with ax1\n",
    "ax2.plot(X2, aps2, color=color)\n",
    "ax2.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "fig.tight_layout()  # otherwise the right y-label is slightly clipped\n",
    "plt.axvline(x=I2, color='k', linestyle = ':')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Improvements\n",
    "\n",
    "In this section, we will show throught PR-curves plots how we have actually improved the model.\n",
    "The first interesting thing is to compute the $\\hat{R}_{12}$ matrix for different models to show how we improved our method. We compute, with a same mask, the following models in the script _improvements.py_:\n",
    "\n",
    "| techniques | Skmeans initialization | Good parameters | Stop criterion |\n",
    "|------------|------------------------|-----------------|----------------|\n",
    "| Model 1    | -                      | -               | -              |\n",
    "| Model 2    | ✓                      | -               | -              |\n",
    "| Model 3    | ✓                      | ✓               | -              |\n",
    "| Model 4    | ✓                      | ✓               | ✓              |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (10,7)\n",
    "\n",
    "M10 = np.load('./tmp/M10.npy')\n",
    "R12 = NMTF.R12\n",
    "n, m = R12.shape\n",
    "\n",
    "for i in range(4):\n",
    "    R12_found = np.load('./tmp/R12_found_' + str(i+1) + '.npy')\n",
    "\n",
    "    R12_2 = []\n",
    "    R12_found_2 = []\n",
    "    for i in range(n):\n",
    "        for j in range(m):\n",
    "            if M10[i, j] ==  0:\n",
    "                R12_2.append(R12[i, j])\n",
    "                R12_found_2.append(R12_found[i, j])\n",
    "    \n",
    "    \n",
    "    precision, recall, _ = metrics.precision_recall_curve(R12_2, R12_found_2)\n",
    "    aps = metrics.average_precision_score(R12_2, R12_found_2)\n",
    "\n",
    "    plt.plot(recall, precision, label=\"Model \" + str(i+1) +\", APS= %0.2f\" % aps)\n",
    "\n",
    "base_precision = np.count_nonzero(R12_2) / len(R12_2)\n",
    "plt.axhline(base_precision, color='grey', linestyle='dashed', label = \"random classifier, APS = %0.2f\" % base_precision)\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.xlabel('recall')\n",
    "plt.ylabel('precision')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also, in order to understand the importance of adding different datasets, we can plot PR curves which corresponds to different $\\hat{R}_{12}$  matrices. The datasets used are:\n",
    "- __Model 1__: $R_{12}$\n",
    "- __Model 2__: $R_{12}, R_{23}$\n",
    "- __Model 3__: $R_{12}, R_{23}, R_{34}$\n",
    "- __Model 4__: $R_{12}, R_{23}, R_{34}, W_3, W_4$\n",
    "- __Model 5__: $R_{12}, R_{23}, R_{34}, W_3, W_4, R_{25}$\n",
    "\n",
    "For that, we created new classes in the file *method_NMTF_DatasetContribution.py* and run the script *DatasetContribution.py*. The results are saved and reloaded here in order to display the following plot.\n",
    "\n",
    "We choose the final hyperparameters : $(k_1, k_2, k_3, k_4, k_5) = (500, 141, 500, 500, 300)$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (10,7)\n",
    "\n",
    "M10 = np.load('./tmp/M10.npy')\n",
    "R12 = NMTF.R12\n",
    "n, m = R12.shape\n",
    "\n",
    "for p in range(5):\n",
    "    R12_found = np.load('./tmp/R12_found_model' + str(p+1) + '_dataset.npy')\n",
    "\n",
    "    R12_2 = []\n",
    "    R12_found_2 = []\n",
    "    for i in range(n):\n",
    "        for j in range(m):\n",
    "            if M10[i, j] ==  0:\n",
    "                R12_2.append(R12[i, j])\n",
    "                R12_found_2.append(R12_found[i, j])\n",
    "    \n",
    "    \n",
    "    precision, recall, _ = metrics.precision_recall_curve(R12_2, R12_found_2)\n",
    "    aps = metrics.average_precision_score(R12_2, R12_found_2)\n",
    "\n",
    "    plt.plot(recall, precision, label=\"Model \" + str(p+1) +\", APS= %0.3f\" % aps)\n",
    "\n",
    "base_precision = np.count_nonzero(R12_2) / len(R12_2)\n",
    "plt.axhline(base_precision, color='grey', linestyle='dashed', label = \"random classifier, APS = %0.3f\" % base_precision)\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.xlabel('recall')\n",
    "plt.ylabel('precision')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Final Results\n",
    "\n",
    "In this section, we will briefly explore the final results provided by our method.\n",
    "We will first apply the NMTF method to all our data, with the best hyperparameters and optimizations discussed.\n",
    "Running the following code, we compute and save the matrices $\\hat{R}_{12}$ and $\\hat{R}_{25}$ that we will use as results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "n, m = NMTF.R12.shape\n",
    "M = np.ones((n, m))\n",
    "K = [500, 141, 500, 500, 300]\n",
    "\n",
    "nmtf_final = NMTF('skmeans', K, M)\n",
    "J = []\n",
    "epsilon = 2e-2\n",
    "\n",
    "nmtf_final.initialize()\n",
    "J.append(nmtf_final.loss())\n",
    "\n",
    "for i in range(30):\n",
    "    nmtf_final.iterate()\n",
    "    J.append(nmtf_final.loss())\n",
    "    \n",
    "    if ((J[-2] - J[-1]) / J[-1]) < epsilon:\n",
    "        break\n",
    "        \n",
    "#np.save('./tmp/R12_final', np.linalg.multi_dot([nmtf_final.G1, nmtf_final.S12, nmtf_final.G2.transpose()]))\n",
    "#np.save('./tmp/R25_final', np.linalg.multi_dot([nmtf_final.G2, nmtf_final.S25, nmtf_final.G5.transpose()]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that this matrices are saved, we will use them to plot the following results. We first load the results and also the names of drugs, labels and diseases corresponding the the different axis of the matrices $\\hat{R}_{12}$ and $\\hat{R}_{25}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "R12 = sparse.load_npz('./tmp/R12.npz').toarray()\n",
    "R25 = sparse.load_npz('./tmp/R25.npz').toarray()\n",
    "\n",
    "R12_found = np.load('./tmp/R12_final.npy')\n",
    "R25_found = np.load('./tmp/R25_final.npy')\n",
    "\n",
    "n1, m1 = R12.shape\n",
    "n2, m2 = R25.shape\n",
    "\n",
    "new_links_R12 = np.multiply(np.ones((n1, m1)) - R12, R12_found)\n",
    "new_links_R25 = np.multiply(np.ones((n2, m2)) - R25, R25_found)\n",
    "\n",
    "with open('./data/Drugs.txt', \"r\") as d:\n",
    "    reader = csv.reader(d)\n",
    "    dr = list(reader)\n",
    "\n",
    "drugs = [element[0] for element in dr] #drugs is the list of drug labels\n",
    "n_drugs = len(drugs)\n",
    "d.close()\n",
    "\n",
    "with open('./data/DrugDisease.csv', \"r\") as d:\n",
    "    d.readline()\n",
    "    reader = csv.reader(d)\n",
    "    diseases = []\n",
    "    edgesdd = []\n",
    "    for row in reader:\n",
    "        dd = row[0].split(';')\n",
    "        diseases.append(dd[1].lstrip())\n",
    "        edgesdd.append((dd[0], dd[1].lstrip()))\n",
    "\n",
    "diseases = list(set(diseases))\n",
    "diseases.sort()\n",
    "diseases.pop()\n",
    "n_diseases = len(diseases)\n",
    "d.close()\n",
    "\n",
    "with open('./data/LabelsDrugs.txt', \"r\") as f:\n",
    "    f.readline()\n",
    "    LabelToDrug = [element.split('\\t') for element in f.readlines()]\n",
    "    labels = [i[2][:-1] for i in LabelToDrug if i[0] in drugs]\n",
    "    drugnames_l = [i[1] for i in LabelToDrug if i[0] in drugs]\n",
    "\n",
    "drugnames = []\n",
    "\n",
    "for i in range(len(drugs)):\n",
    "    drug = drugs[i]\n",
    "    for l in LabelToDrug:\n",
    "        if l[0] == drug:\n",
    "            drugnames.append(l[1])\n",
    "            break\n",
    "\n",
    "\n",
    "labels = list(set(labels))\n",
    "labels.sort() #labels is a list of disease labels, sorted in the alphabetical order\n",
    "n_labels = len(labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next few lines create a table in which every new links are describes by their *Drug ID*, *Drug Name*, *Label* and *Score*. The *Score* corresponds to the coefficient found in the new computed matrix (through the NMTF method). Here, we only plot the first 100 links found in $\\hat{R}_{12}$, ordered by their *Score*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "index_new_links_R12 = np.argsort(new_links_R12.flatten())\n",
    "\n",
    "new_drugs = []\n",
    "new_labels = []\n",
    "prob = []\n",
    "new_drugnames = []\n",
    "for i in range(1, 21):\n",
    "    new_drugs.append(drugs[index_new_links_R12[-i] % m1])\n",
    "    new_labels.append(labels[index_new_links_R12[-i] // m1])\n",
    "    prob.append(new_links_R12[index_new_links_R12[-i] // m1, index_new_links_R12[-i] % m1])\n",
    "    new_drugnames.append(drugnames[index_new_links_R12[-i] % m1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<table><tr><td>ID</td><td>Drug ID</td><td>Drug name</td><td>Label</td><td>Score</td></tr><tr><td>1</td><td>DB12010</td><td>Fostamatinib</td><td>Cardiovascular Agents</td><td>3.37</td></tr><tr><td>2</td><td>DB14533</td><td>Zinc chloride</td><td>Alcohols</td><td>1.43</td></tr><tr><td>3</td><td>DB00628</td><td>Clorazepate</td><td>Nervous System</td><td>1.11</td></tr><tr><td>4</td><td>DB01224</td><td>Quetiapine</td><td>Neurotransmitter Agents</td><td>1.06</td></tr><tr><td>5</td><td>DB00659</td><td>Acamprosate</td><td>Central Nervous System Depressants</td><td>1.06</td></tr><tr><td>6</td><td>DB01238</td><td>Aripiprazole</td><td>Neurotransmitter Agents</td><td>1.04</td></tr><tr><td>7</td><td>DB12267</td><td>Brigatinib</td><td>Cytochrome P-450 Enzyme Inducers</td><td>0.98</td></tr><tr><td>8</td><td>DB14185</td><td>Aripiprazole lauroxil</td><td>Neurotransmitter Agents</td><td>0.92</td></tr><tr><td>9</td><td>DB12010</td><td>Fostamatinib</td><td>Antiarrhythmic agents</td><td>0.91</td></tr><tr><td>10</td><td>DB00825</td><td>Levomenthol</td><td>Cardiovascular Agents</td><td>0.91</td></tr><tr><td>11</td><td>DB13961</td><td>Fish oil</td><td>Cardiovascular Agents</td><td>0.90</td></tr><tr><td>12</td><td>DB13746</td><td>Bioallethrin</td><td>Membrane Transport Modulators</td><td>0.89</td></tr><tr><td>13</td><td>DB00005</td><td>Etanercept</td><td>Antineoplastic and Immunomodulating Agents</td><td>0.88</td></tr><tr><td>14</td><td>DB04855</td><td>Dronedarone</td><td>Agents causing hyperkalemia</td><td>0.88</td></tr><tr><td>15</td><td>DB14724</td><td>Emapalumab</td><td>Blood Proteins</td><td>0.87</td></tr><tr><td>16</td><td>DB00157</td><td>NADH</td><td>Biological Factors</td><td>0.87</td></tr><tr><td>17</td><td>DB00825</td><td>Levomenthol</td><td>Membrane Transport Modulators</td><td>0.87</td></tr><tr><td>18</td><td>DB11855</td><td>Revefenacin</td><td>Agents producing tachycardia</td><td>0.87</td></tr><tr><td>19</td><td>DB00313</td><td>Valproic Acid</td><td>Cytochrome P-450 CYP3A4 Substrates</td><td>0.86</td></tr><tr><td>20</td><td>DB00754</td><td>Ethotoin</td><td>Cytochrome P-450 Enzyme Inducers</td><td>0.86</td></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Markdown\n",
    "\n",
    "table = [[i+1, new_drugs[i], new_drugnames[i], new_labels[i], \"%.2f\" % prob[i]] for i in range(len(prob))]\n",
    "table.insert(0, [\"ID\", \"Drug ID\", \"Drug name\", \"Label\", \"Score\"])\n",
    "\n",
    "html = [\"<table>\"]\n",
    "for row in table:\n",
    "    html.append(\"<tr>\")\n",
    "            \n",
    "    for col in row:\n",
    "        html.append(\"<td>{0}</td>\".format(col))\n",
    "            \n",
    "    html.append(\"</tr>\")\n",
    "html.append(\"</table>\")\n",
    "\n",
    "Markdown(''.join(html))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also easy to do the same for eventual new links found between diseases and drugs, which are described by $\\hat{R}_{25}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "index_new_links_R25 = np.argsort(new_links_R25.flatten())\n",
    "\n",
    "new_drugs_2 = []\n",
    "new_diseases = []\n",
    "prob_2 = []\n",
    "new_drugnames_2 = []\n",
    "for i in range(1, 21):\n",
    "    new_drugs_2.append(drugs[index_new_links_R25[-i] // m2])\n",
    "    new_diseases.append(diseases[index_new_links_R25[-i] % m2])\n",
    "    prob_2.append(new_links_R25[index_new_links_R25[-i] // m2, index_new_links_R25[-i] % m2])\n",
    "    new_drugnames_2.append(drugnames[index_new_links_R25[-i] // m2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<table><tr><td>ID</td><td>Drug ID</td><td>Drug name</td><td>Disease</td><td>Score</td></tr><tr><td>1</td><td>DB13025</td><td>Tiapride</td><td>Schizophrenia</td><td>1.03</td></tr><tr><td>2</td><td>DB08922</td><td>Perospirone</td><td>Schizophrenia</td><td>0.79</td></tr><tr><td>3</td><td>DB01340</td><td>Cilazapril</td><td>Hypertension</td><td>0.79</td></tr><tr><td>4</td><td>DB00275</td><td>Olmesartan</td><td>Hypertension</td><td>0.74</td></tr><tr><td>5</td><td>DB01415</td><td>Ceftibuten</td><td>Bacterial infections</td><td>0.69</td></tr><tr><td>6</td><td>DB00796</td><td>Candesartan cilexetil</td><td>Hypertension</td><td>0.69</td></tr><tr><td>7</td><td>DB00948</td><td>Mezlocillin</td><td>Bacterial infections</td><td>0.68</td></tr><tr><td>8</td><td>DB00560</td><td>Tigecycline</td><td>Bacterial infections</td><td>0.68</td></tr><tr><td>9</td><td>DB00607</td><td>Nafcillin</td><td>Bacterial infections</td><td>0.67</td></tr><tr><td>10</td><td>DB01139</td><td>Cefapirin</td><td>Bacterial infections</td><td>0.66</td></tr><tr><td>11</td><td>DB00229</td><td>Cefotiam</td><td>Bacterial infections</td><td>0.66</td></tr><tr><td>12</td><td>DB00417</td><td>Phenoxymethylpenicillin</td><td>Bacterial infections</td><td>0.66</td></tr><tr><td>13</td><td>DB01605</td><td>Pivmecillinam</td><td>Bacterial infections</td><td>0.66</td></tr><tr><td>14</td><td>DB09286</td><td>Pipamperone</td><td>Schizophrenia</td><td>0.65</td></tr><tr><td>15</td><td>DB01326</td><td>Cefamandole</td><td>Bacterial infections</td><td>0.65</td></tr><tr><td>16</td><td>DB09242</td><td>Moxonidine</td><td>Hypertension</td><td>0.64</td></tr><tr><td>17</td><td>DB09324</td><td>Sulbactam</td><td>Bacterial infections</td><td>0.63</td></tr><tr><td>18</td><td>DB01301</td><td>Rolitetracycline</td><td>Bacterial infections</td><td>0.62</td></tr><tr><td>19</td><td>DB01112</td><td>Cefuroxime</td><td>Bacterial infections</td><td>0.62</td></tr><tr><td>20</td><td>DB01061</td><td>Azlocillin</td><td>Bacterial infections</td><td>0.62</td></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table2 = [[i+1, new_drugs_2[i], new_drugnames_2[i], new_diseases[i], \"%.2f\" % prob_2[i]] for i in range(len(prob_2))]\n",
    "table2.insert(0, [\"ID\", \"Drug ID\", \"Drug name\", \"Disease\", \"Score\"])\n",
    "\n",
    "html2 = [\"<table>\"]\n",
    "for row in table2:\n",
    "    html2.append(\"<tr>\")\n",
    "            \n",
    "    for col in row:\n",
    "        html2.append(\"<td>{0}</td>\".format(col))\n",
    "            \n",
    "    html2.append(\"</tr>\")\n",
    "html2.append(\"</table>\")\n",
    "\n",
    "Markdown(''.join(html2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a comparison with the initial data, we can also show, given a certain threshold $\\delta$, the number of rediscovered and new links. Especially, it can be interesting to visualize, for a given threshold, the number of:\n",
    "- **True positive (TP)**: links which are rediscovered (already in the graph);\n",
    "- **False negative (FN)**: links which are not found by the NMTF algorithm but were in the graph;\n",
    "- **False Negative (FN)**: potential new associations found by the NMTF algorithm.\n",
    "\n",
    "The following code displays a visual representation of those quantities for different thresholds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "R12 = sparse.load_npz('./tmp/R12.npz').toarray()\n",
    "R12_found = np.load('./tmp/R12_final.npy')\n",
    "\n",
    "delta = [0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]\n",
    "n, m = R12_found.shape\n",
    "R12_delta = np.zeros((n,m))\n",
    "T = [np.sum(R12)]*len(delta)\n",
    "TP, FP = [], []\n",
    "\n",
    "plt.rcdefaults()\n",
    "fig, ax = plt.subplots()\n",
    "for l in range(len(delta)):\n",
    "    for i in range(n):\n",
    "        for j in range(m):\n",
    "            R12_delta[i,j] = R12_found[i,j] > delta[l]\n",
    "\n",
    "    R12_TP = np.multiply(R12_delta, R12)\n",
    "    TP.append(np.sum(R12_TP))\n",
    "    FP.append(np.sum(R12_delta) - np.sum(R12_TP) + T[0])\n",
    "\n",
    "y_pos = np.arange(len(delta))\n",
    "\n",
    "ax.barh(y_pos, FP,label = 'new links (FP)', color = 'goldenrod')\n",
    "ax.barh(y_pos, T, label = 'False Negative (FN)', color = 'dodgerblue')\n",
    "ax.barh(y_pos, TP, label = 'True positive (TP)', color = 'lightgreen')\n",
    "\n",
    "delta_label = ['delta = {}'.format(l) for l in delta]\n",
    "ax.set_yticks(y_pos)\n",
    "ax.set_yticklabels(delta_label)\n",
    "ax.invert_yaxis()  # labels read top-to-bottom\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a given threshold $\\delta$, this data can also be plot per label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "R12 = sparse.load_npz('./tmp/R12.npz').toarray()\n",
    "R12_found = np.load('./tmp/R12_final.npy')\n",
    "plt.rcParams[\"figure.figsize\"] = (30,10)\n",
    "\n",
    "delta = 0.4\n",
    "n, m = R12_found.shape\n",
    "\n",
    "for i in range(n):\n",
    "    for j in range(m):\n",
    "        R12_found[i,j] = R12_found[i,j] > delta\n",
    "\n",
    "R12_TP = np.multiply(R12_found, R12)\n",
    "\n",
    "bar = np.sum(R12, axis=1)\n",
    "Index = np.argsort(bar)\n",
    "\n",
    "bar.sort()\n",
    "bar_TP = []\n",
    "bar_new = []\n",
    "\n",
    "X = np.arange(len(bar))\n",
    "\n",
    "for i in range(len(Index)):\n",
    "    bar_TP.append(np.sum(R12_TP[Index[i]]))\n",
    "    bar_new.append(np.sum(R12_found[Index[i]]) - np.sum(R12_TP[Index[i]]))\n",
    "    \n",
    "plt.rcParams[\"figure.figsize\"] = (30,10)\n",
    "plt.bar(X, bar, label = 'False Negative (FN)')\n",
    "plt.bar(X, bar_TP, label = 'True positive (TP)')\n",
    "plt.bar(X, bar_new, bottom=bar, label = 'new links (FP)')\n",
    "plt.legend()\n",
    "plt.xlabel('Diseases')\n",
    "plt.ylabel('Number of associated Drugs')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot is particularly interesting as it shows that, *a priori*, it is hard to find a correlation between:\n",
    "- The number of original links and the number of new links found\n",
    "- The proportion of TP (**<span style=\"color:orange\">in orange</span>**) and the proportion of FP (**<span style=\"color:green\">in green</span>**)."
   ]
  }
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