molbloom_fpr_study.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Jgmedina95/molbloom-paper/blob/main/molbloom_fpr_study.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install molbloom\n"
      ],
      "metadata": {
        "id": "AcGB3vRYT7vv",
        "outputId": "cdf1c245-2fde-4acd-8a47-4c8b2400021d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "AcGB3vRYT7vv",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting molbloom\n",
            "  Downloading molbloom-2.1.0-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (9.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.3/9.3 MB\u001b[0m \u001b[31m29.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: importlib-resources in /usr/local/lib/python3.9/dist-packages (from molbloom) (5.12.0)\n",
            "Requirement already satisfied: zipp>=3.1.0 in /usr/local/lib/python3.9/dist-packages (from importlib-resources->molbloom) (3.15.0)\n",
            "Installing collected packages: molbloom\n",
            "Successfully installed molbloom-2.1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install rdkit"
      ],
      "metadata": {
        "id": "-L5C6-vLUFid",
        "outputId": "2dcb5f77-9a0d-4f51-c21f-a6a19d2efffd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "-L5C6-vLUFid",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting rdkit\n",
            "  Downloading rdkit-2022.9.5-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (29.4 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m29.4/29.4 MB\u001b[0m \u001b[31m39.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.9/dist-packages (from rdkit) (1.22.4)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.9/dist-packages (from rdkit) (8.4.0)\n",
            "Installing collected packages: rdkit\n",
            "Successfully installed rdkit-2022.9.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "601dc863",
      "metadata": {
        "id": "601dc863"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.font_manager as font_manager\n",
        "import urllib.request\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.colors as colors\n",
        "from matplotlib.ticker import FuncFormatter\n",
        "import numpy as np\n",
        "import math\n",
        "import os\n",
        "import urllib\n",
        "from dataclasses import dataclass\n",
        "import json\n",
        "import sys\n",
        "from molbloom import CustomFilter, canon\n",
        "from rdkit import DataStructs\n",
        "from rdkit import Chem\n",
        "from rdkit.Chem import AllChem, MACCSkeys, AllChem, MACCSkeys, rdMolDescriptors\n",
        "import time\n",
        "from urllib.request import urlopen\n",
        "from urllib.error import URLError, HTTPError\n",
        "import json\n",
        "#np.random.seed(0)\n",
        "urllib.request.urlretrieve('https://github.com/google/fonts/raw/main/ofl/ibmplexmono/IBMPlexMono-Regular.ttf', 'IBMPlexMono-Regular.ttf')\n",
        "fe = font_manager.FontEntry(\n",
        "    fname='IBMPlexMono-Regular.ttf',\n",
        "    name='plexmono')\n",
        "font_manager.fontManager.ttflist.append(fe)\n",
        "plt.rcParams.update({'axes.facecolor':'#f5f4e9', \n",
        "            'grid.color' : '#AAAAAA', \n",
        "            'axes.edgecolor':'#333333', \n",
        "            'figure.facecolor':'#FFFFFF', \n",
        "            'axes.grid': False,\n",
        "            'axes.prop_cycle':   plt.cycler('color', plt.cm.Dark2.colors),\n",
        "            'font.family': fe.name,\n",
        "            'figure.figsize': (3.5,3.5 / 1.2),\n",
        "            'ytick.left': True,\n",
        "            'xtick.bottom': True   \n",
        "           })"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "m-m71sQJUn2-",
        "outputId": "80cc3ac6-d139-40c3-868c-02703b072b48",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "m-m71sQJUn2-",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "69f4e013",
      "metadata": {
        "id": "69f4e013"
      },
      "outputs": [],
      "source": [
        "with open(\"/content/drive/MyDrive/COCONUT_DB.smi\", \"r\") as f:\n",
        "    smi_list = f.readlines()\n",
        "#smi_list"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from rdkit import RDLogger\n",
        "RDLogger.DisableLog('rdApp.warning') #this is for cleaner presentation. A lot of messages of the type: [13:19:48] Explicit valence for atom # 3 B, 4, is greater than permitted"
      ],
      "metadata": {
        "id": "iICis9HSUxmE"
      },
      "id": "iICis9HSUxmE",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "054dc242",
      "metadata": {
        "scrolled": true,
        "id": "054dc242"
      },
      "outputs": [],
      "source": [
        "mols = []\n",
        "can_smiles = []\n",
        "for i,smi in enumerate(smi_list):\n",
        "  mol = Chem.MolFromSmiles(smi.split(\" \")[0])\n",
        "  if mol:\n",
        "    mols.append(mol)\n",
        "    can_smiles.append(Chem.MolToSmiles(mol,True))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "12c2a782",
      "metadata": {
        "id": "12c2a782"
      },
      "outputs": [],
      "source": [
        "#this takes some minutes\n",
        "third_fps = [rdMolDescriptors.GetHashedAtomPairFingerprintAsBitVect(mol) for mol in mols]\n",
        "morgan_fps = [AllChem.GetMorganFingerprintAsBitVect(mol, 2) for mol in mols]\n",
        "rdkfingerprints = [Chem.RDKFingerprint(mol) for mol in mols]\n",
        "MACCfps = [MACCSkeys.GenMACCSKeys(mol)for mol in mols]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e8ed5a7c",
      "metadata": {
        "id": "e8ed5a7c"
      },
      "outputs": [],
      "source": [
        "#python implementation of bloom filter with fps\n",
        "\n",
        "def create_empty_bloom_filter(size):\n",
        "    return np.zeros(size)\n",
        "\n",
        "def hashed_indexes(h1,h2,size):\n",
        "  indexes = []\n",
        "  for i in range(max(8, min(64, int(size / int(406919/2) * math.log(2))))):\n",
        "     indexes.append(((h1) + i*(h2))%size)\n",
        "  return indexes\n",
        "\n",
        "def add_molecules(fp1,fp2,bf,size):\n",
        "  h1 = int(DataStructs.BitVectToText(fp1),base=2)\n",
        "  h2 = int(DataStructs.BitVectToText(fp2),base=2)\n",
        "  for index in hashed_indexes(h1,h2,size):\n",
        "    bf[index]=1\n",
        "  return bf\n",
        "  \n",
        "def check_molecule(fp1, fp2, bf, size):\n",
        "    h1 = int(DataStructs.BitVectToText(fp1), base=2)\n",
        "    h2 = int(DataStructs.BitVectToText(fp2), base=2)\n",
        "    for index in hashed_indexes(h1, h2, size):\n",
        "        if bf[index] == 0:\n",
        "            return False\n",
        "    return True\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2380b213",
      "metadata": {
        "id": "2380b213"
      },
      "outputs": [],
      "source": [
        "#setting parameters for tests\n",
        "bloom_size = [10,100,1000,10000,50000,100000,150000,200000,250000,300000,350000,400000,450000,\n",
        "             500000,550000,600000,650000,700000,750000,800000,850000,900000,950000,1000000,2000000,\n",
        "              3000000,4000000,5000000,10_000_000,100_000_000]\n",
        "split = int(len(mols)/2)\n",
        "fpr_fps = []\n",
        "is_in_fps = []\n",
        "def test_wraper(fps1,fps2,size_range,split_index):\n",
        "  fpr_fps=[]\n",
        "  for size in size_range:\n",
        "    is_not_fps = []\n",
        "    bf = create_empty_bloom_filter(size)\n",
        "    for i, (fp1,fp2) in enumerate(zip(fps1,fps2)):\n",
        "      if i <split_index:\n",
        "        bf = add_molecules(fp1,fp2,bf,size)\n",
        "      else:\n",
        "        is_not_fps.append(check_molecule(fp1,fp2,bf,size))\n",
        "    print(f'false positive rate fps:size {size:1.2E} {100-((len(is_not_fps)-sum(is_not_fps))*100/len(is_not_fps))}')\n",
        "    fpr_fps.append(100-((len(is_not_fps)-sum(is_not_fps))*100/len(is_not_fps)))\n",
        "  return fpr_fps\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2b36a236",
      "metadata": {
        "id": "2b36a236",
        "outputId": "e41e1315-87f1-4899-f5f7-49378b99c247",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "false positive rate fps:size 1.00E+01 100.0\n",
            "false positive rate fps:size 1.00E+02 100.0\n",
            "false positive rate fps:size 1.00E+03 100.0\n",
            "false positive rate fps:size 1.00E+04 99.61319178216849\n",
            "false positive rate fps:size 5.00E+04 97.84773419836823\n",
            "false positive rate fps:size 1.00E+05 96.7394082374914\n",
            "false positive rate fps:size 1.50E+05 95.179396441561\n",
            "false positive rate fps:size 2.00E+05 94.64022412267768\n",
            "false positive rate fps:size 2.50E+05 93.30777548412465\n",
            "false positive rate fps:size 3.00E+05 92.1832301189423\n",
            "false positive rate fps:size 3.50E+05 91.86375700383368\n",
            "false positive rate fps:size 4.00E+05 90.60552442740588\n",
            "false positive rate fps:size 4.50E+05 90.4855991349651\n",
            "false positive rate fps:size 5.00E+05 88.93492578393787\n",
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            "false positive rate fps:size 6.00E+05 87.37098201120614\n",
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            "false positive rate fps:size 5.50E+05 97.11540351911924\n",
            "false positive rate fps:size 6.00E+05 83.52944067630001\n",
            "false positive rate fps:size 6.50E+05 93.72210753956551\n",
            "false positive rate fps:size 7.00E+05 82.56758085127298\n",
            "false positive rate fps:size 7.50E+05 89.06173203578099\n",
            "false positive rate fps:size 8.00E+05 74.8535338641502\n",
            "false positive rate fps:size 8.50E+05 82.64474589599922\n",
            "false positive rate fps:size 9.00E+05 74.37284970018678\n",
            "false positive rate fps:size 9.50E+05 75.24427405878305\n",
            "false positive rate fps:size 1.00E+06 69.2140961368328\n",
            "false positive rate fps:size 2.00E+06 42.67276123070874\n",
            "false positive rate fps:size 3.00E+06 16.693207510075695\n",
            "false positive rate fps:size 4.00E+06 19.859431829352204\n",
            "false positive rate fps:size 5.00E+06 1.4523739309938009\n",
            "false positive rate fps:size 1.00E+07 0.059962646220384386\n",
            "false positive rate fps:size 1.00E+08 0.028998328909864313\n",
            "false positive rate fps:size 1.00E+01 100.0\n",
            "false positive rate fps:size 1.00E+02 100.0\n",
            "false positive rate fps:size 1.00E+03 100.0\n",
            "false positive rate fps:size 1.00E+04 100.0\n",
            "false positive rate fps:size 5.00E+04 100.0\n",
            "false positive rate fps:size 1.00E+05 99.99508502899833\n",
            "false positive rate fps:size 1.50E+05 99.8953111176644\n",
            "false positive rate fps:size 2.00E+05 97.44519807333137\n",
            "false positive rate fps:size 2.50E+05 97.41570824732133\n",
            "false positive rate fps:size 3.00E+05 94.44018480290967\n",
            "false positive rate fps:size 3.50E+05 90.41187456994004\n",
            "false positive rate fps:size 4.00E+05 82.32134080408926\n",
            "false positive rate fps:size 4.50E+05 79.34483436547724\n",
            "false positive rate fps:size 5.00E+05 72.84871719256856\n",
            "false positive rate fps:size 5.50E+05 66.92961761525606\n",
            "false positive rate fps:size 6.00E+05 61.076870146466135\n",
            "false positive rate fps:size 6.50E+05 54.66627346898653\n",
            "false positive rate fps:size 7.00E+05 48.982109505553915\n",
            "false positive rate fps:size 7.50E+05 43.80173006979259\n",
            "false positive rate fps:size 8.00E+05 45.106654870736264\n",
            "false positive rate fps:size 8.50E+05 34.658409515383866\n",
            "false positive rate fps:size 9.00E+05 30.737737147350828\n",
            "false positive rate fps:size 9.50E+05 27.8123464071562\n",
            "false positive rate fps:size 1.00E+06 26.676988105770178\n",
            "false positive rate fps:size 2.00E+06 6.820996756119143\n",
            "false positive rate fps:size 3.00E+06 4.837314459844691\n",
            "false positive rate fps:size 4.00E+06 4.694780300796225\n",
            "false positive rate fps:size 5.00E+06 4.571414528654287\n",
            "false positive rate fps:size 1.00E+07 4.568957043153446\n",
            "false positive rate fps:size 1.00E+08 4.568957043153446\n"
          ]
        }
      ],
      "source": [
        "##This takes a while, 31 different filter sizes for each test.\n",
        "#print(\"morgan_fps\", \"atom_pair_fingerprints\")\n",
        "fpr_fps1 = test_wraper(morgan_fps,third_fps,bloom_size,split)\n",
        "#print(\"morgan_fps\", \"rdkfingerprints\")\n",
        "fpr_fps2 = test_wraper(morgan_fps,rdkfingerprints,bloom_size,split)\n",
        "#print(\"morgan_fps\",\"MACCS\")\n",
        "fpr_fps3 = test_wraper(morgan_fps,MACCfps,bloom_size,split)\n",
        "#print(\"atom_pair_fingerprints\", \"RDK_fingerprints\")\n",
        "fpr_fps4 = test_wraper(third_fps,rdkfingerprints,bloom_size,split)\n",
        "#print(\"atom_pair_fingerprints\", \"MACCfps\")\n",
        "fpr_fps5 = test_wraper(third_fps,MACCfps,bloom_size,split)\n",
        "#print(\"rdk_fingerprints\",\"MACCS\")\n",
        "fpr_fps6 = test_wraper(rdkfingerprints,MACCfps,bloom_size,split)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b7848403",
      "metadata": {
        "id": "b7848403",
        "outputId": "c78a3f6a-7930-422b-d2e3-95b96f7e0035",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10 203459\n",
            "false positive rate smiles: 100.0\n",
            "100 203459\n",
            "false positive rate smiles: 100.0\n",
            "1000 203459\n",
            "false positive rate smiles: 100.0\n",
            "10000 203459\n",
            "false positive rate smiles: 100.0\n",
            "50000 203459\n",
            "false positive rate smiles: 100.0\n",
            "100000 203459\n",
            "false positive rate smiles: 100.0\n",
            "150000 203459\n",
            "false positive rate smiles: 99.99115300871429\n",
            "200000 203459\n",
            "false positive rate smiles: 99.76260573383335\n",
            "250000 203459\n",
            "false positive rate smiles: 98.7506082306509\n",
            "300000 203459\n",
            "false positive rate smiles: 96.61455133466694\n",
            "350000 203459\n",
            "false positive rate smiles: 92.47907440811171\n",
            "400000 203459\n",
            "false positive rate smiles: 87.26819654082641\n",
            "450000 203459\n",
            "false positive rate smiles: 80.355255850073\n",
            "500000 203459\n",
            "false positive rate smiles: 72.9252576686212\n",
            "550000 203459\n",
            "false positive rate smiles: 65.50410647845513\n",
            "600000 203459\n",
            "false positive rate smiles: 57.64208022255098\n",
            "650000 203459\n",
            "false positive rate smiles: 50.61658614266265\n",
            "700000 203459\n",
            "false positive rate smiles: 43.70462845094097\n",
            "750000 203459\n",
            "false positive rate smiles: 38.10939796224301\n",
            "800000 203459\n",
            "false positive rate smiles: 32.388835096997425\n",
            "850000 203459\n",
            "false positive rate smiles: 27.986473933323182\n",
            "900000 203459\n",
            "false positive rate smiles: 23.79103406583144\n",
            "950000 203459\n",
            "false positive rate smiles: 20.277304026855532\n",
            "1000000 203459\n",
            "false positive rate smiles: 17.400557360451003\n",
            "2000000 203459\n",
            "false positive rate smiles: 0.9240190898411953\n",
            "3000000 203459\n",
            "false positive rate smiles: 0.07863992253967922\n",
            "4000000 203459\n",
            "false positive rate smiles: 0.009338490801582111\n",
            "5000000 203459\n",
            "false positive rate smiles: 0.0009829990317484771\n",
            "10000000 203459\n",
            "false positive rate smiles: 0.0004914995158742386\n",
            "100000000 203459\n",
            "false positive rate smiles: 0.0\n"
          ]
        }
      ],
      "source": [
        "fpr_smi = []\n",
        "for size in bloom_size:\n",
        "  bloom_smi = CustomFilter(size,200000,\"myfilter\")\n",
        "  is_not_smiles = []\n",
        "  for i,smi in enumerate(can_smiles):\n",
        "    if i <split:\n",
        "        bloom_smi.add(smi)\n",
        "    if i > split:       \n",
        "      is_not_smiles.append(bloom_smi.__contains__(smi))\n",
        "  fpr_smi.append(100-(len(is_not_smiles)-sum(is_not_smiles))*100/len(is_not_smiles))\n",
        "  print(size,len(is_not_smiles))\n",
        "  print(f'false positive rate smiles: {100-(len(is_not_smiles)-sum(is_not_smiles))*100/len(is_not_smiles)}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "63a59944",
      "metadata": {
        "id": "63a59944"
      },
      "outputs": [],
      "source": [
        "def add_molecules_md(fp1,fp2,bf,size):\n",
        "\n",
        "  for index in hashed_indexes(fp1,fp2,size):\n",
        "    bf[index]=1\n",
        "  return bf\n",
        "  \n",
        "def check_molecule_md(fp1, fp2, bf, size):\n",
        "\n",
        "    for index in hashed_indexes(fp1, fp2, size):\n",
        "        if bf[index] == 0:\n",
        "            return False\n",
        "    return True\n",
        "\n",
        "def test_wraper_md(fps1,fps2,size_range,split_index):\n",
        "    print()\n",
        "    fpr_fps=[]\n",
        "    for size in size_range:\n",
        "        is_not_fps = []\n",
        "        bf = create_empty_bloom_filter(size)\n",
        "        for i, (fp1,fp2) in enumerate(zip(fps1,fps2)):\n",
        "            if i < split_index:\n",
        "                bf = add_molecules_md(fp1,fp2,bf,size)\n",
        "            else:\n",
        "                is_not_fps.append(check_molecule_md(fp1,fp2,bf,size))\n",
        "\n",
        "        print(f'false positive rate fps:size {size:1.2E} {100-((len(is_not_fps)-sum(is_not_fps))*100/len(is_not_fps))}')\n",
        "        fpr_fps.append(100-((len(is_not_fps)-sum(is_not_fps))*100/len(is_not_fps)))\n",
        "    return fpr_fps"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import hashlib"
      ],
      "metadata": {
        "id": "rT4Z2nBp2yBd"
      },
      "id": "rT4Z2nBp2yBd",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ba87bd80",
      "metadata": {
        "id": "ba87bd80",
        "outputId": "0ad03460-fa6a-4a5f-c02c-b002c4afdfc3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "false positive rate fps:size 1.00E+01 100.0\n",
            "false positive rate fps:size 1.00E+02 100.0\n",
            "false positive rate fps:size 1.00E+03 100.0\n",
            "false positive rate fps:size 1.00E+04 100.0\n",
            "false positive rate fps:size 5.00E+04 100.0\n",
            "false positive rate fps:size 1.00E+05 100.0\n",
            "false positive rate fps:size 1.50E+05 99.98328909859431\n",
            "false positive rate fps:size 2.00E+05 99.79160522952914\n",
            "false positive rate fps:size 2.50E+05 98.77224024378256\n",
            "false positive rate fps:size 3.00E+05 96.35210852255972\n",
            "false positive rate fps:size 3.50E+05 92.60051115698417\n",
            "false positive rate fps:size 4.00E+05 87.25302270716602\n",
            "false positive rate fps:size 4.50E+05 80.10714636783644\n",
            "false positive rate fps:size 5.00E+05 72.96520200530817\n",
            "false positive rate fps:size 5.50E+05 65.34306497591663\n",
            "false positive rate fps:size 6.00E+05 57.81775287525804\n",
            "false positive rate fps:size 6.50E+05 50.72151774304532\n",
            "false positive rate fps:size 7.00E+05 43.919197876732525\n",
            "false positive rate fps:size 7.50E+05 37.72387692912612\n",
            "false positive rate fps:size 8.00E+05 32.4805858645434\n",
            "false positive rate fps:size 8.50E+05 28.019266686326546\n",
            "false positive rate fps:size 9.00E+05 23.923621350634036\n",
            "false positive rate fps:size 9.50E+05 20.183819915462493\n",
            "false positive rate fps:size 1.00E+06 17.5208886267571\n",
            "false positive rate fps:size 2.00E+06 0.9367934729185095\n",
            "false positive rate fps:size 3.00E+06 0.07814803892657096\n",
            "false positive rate fps:size 4.00E+06 0.0073724565025088395\n",
            "false positive rate fps:size 5.00E+06 0.0009829942003278802\n",
            "false positive rate fps:size 1.00E+07 0.0\n",
            "false positive rate fps:size 1.00E+08 0.0\n"
          ]
        }
      ],
      "source": [
        "md4 = [int(hashlib.new(\"md4\",smile.encode('utf-16le')).hexdigest(),16) for smile in can_smiles]\n",
        "md5 = [int(hashlib.new(\"md5\",smile.encode('utf-16le')).hexdigest(),16) for smile in can_smiles]\n",
        "fpr_fpsmd = test_wraper_md(md4,md5,bloom_size,split)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5cb760ab",
      "metadata": {
        "id": "5cb760ab"
      },
      "outputs": [],
      "source": [
        "bloom_size = np.array(bloom_size)\n",
        "import math\n",
        "\n",
        "def bloom_filter_false_positive_rate(k, m, n):\n",
        "    \"\"\"\n",
        "    Calculates the theoretical false positive rate of a Bloom filter\n",
        "    with k hash functions and m bits in the filter, given the number\n",
        "    of elements added to the filter.\n",
        "\n",
        "    Args:\n",
        "    - k (int): Number of hash functions\n",
        "    - m (int): Number of bits in the filter\n",
        "    - n (int): Number of elements added to the filter\n",
        "\n",
        "    Returns:\n",
        "    - Theoretical false positive rate of the Bloom filter\n",
        "    \"\"\"\n",
        "    p = (1 - math.exp(-k * n / m)) ** k\n",
        "    return p\n",
        "theoretical_fpr = []\n",
        "for size in bloom_size:\n",
        "    theoretical_fpr.append(100*bloom_filter_false_positive_rate(max(8,min(64,size//(406919//2)*math.log(2))),size,int(406919/2)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "98666f52",
      "metadata": {
        "id": "98666f52",
        "outputId": "893a7432-98ab-424a-f35c-d88b6121a75e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-19-6cdceb490954>:30: MatplotlibDeprecationWarning: The join function was deprecated in Matplotlib 3.6 and will be removed two minor releases later.\n",
            "  axs[i].get_shared_y_axes().join(axs[i], axs[i+3])\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1300x650 with 8 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "\n",
        "# Define the data\n",
        "data = {\n",
        "    \"Morgan-AtomPair\": fpr_fps1,\n",
        "    \"Morgan-RDK\": fpr_fps2,\n",
        "    \"Morgan-MACCS\": fpr_fps3,\n",
        "    \"Atompair-RDK\": fpr_fps4,\n",
        "    \"Atompair-MACCS \": fpr_fps5,\n",
        "    \"RDK-MACCS\": fpr_fps6,\n",
        "    \"Smiles:FNV\": fpr_smi,\n",
        "    \"Smiles:md4-md5\": fpr_fpsmd\n",
        "}\n",
        "\n",
        "color_list = list(colors.TABLEAU_COLORS.values())\n",
        "# Create the subplots\n",
        "fig, axs = plt.subplots(2, 4, figsize=(13, 6.5), sharex=True, sharey=False)\n",
        "axs = axs.flatten()\n",
        "\n",
        "# Plot the data and theoretical values\n",
        "for i, (name, values) in enumerate(data.items()):\n",
        "    axs[i].plot(bloom_size, values, label=name,color=f'{color_list[i]}')\n",
        "    axs[i].plot(bloom_size, theoretical_fpr, linestyle='--',color='black', label='Theoretical FPR')\n",
        "    axs[i].set_xscale('log')\n",
        "    axs[i].set_title(f\"Hash Function Combination {i+1}\")\n",
        "    axs[i].set_xlabel(\"Size (bits)\")\n",
        "    axs[i].legend()\n",
        "\n",
        "    # Set sharey to True for the first column\n",
        "    if i % 4 == 0:\n",
        "        axs[i].set_ylabel(\"False Positive Rate\")\n",
        "        axs[i].get_shared_y_axes().join(axs[i], axs[i+3])\n",
        "\n",
        "# Remove y-axis for the remaining columns\n",
        "#for i in range(1, 4):\n",
        "#    for j in range(0, 2):\n",
        "#        axs[i*4+j].get_yaxis().set_visible(False)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8f95cdd2",
      "metadata": {
        "id": "8f95cdd2"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "molbloom",
      "language": "python",
      "name": "molbloom"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.13"
    },
    "colab": {
      "provenance": [],
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "gpuClass": "standard"
  },
  "nbformat": 4,
  "nbformat_minor": 5
}