MMseqs2-Clustering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d6f464eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%bash\n",
    "\n",
    "# if [ -f '/home/rafsanjani/backup-research' mmseqs/util/bash-completion.sh ]; then\n",
    "#     source '/home/rafsanjani/backup-research' mmseqs2/util/bash-completion.sh\n",
    "# fi"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddafd28f-b46a-4785-9c15-51853fbc4994",
   "metadata": {},
   "source": [
    "### Simple, Fast and Agile:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "id": "ea7330d2-b1dc-4c9a-a026-63a86ebc9901",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import esm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "id": "8a79139c-142b-4214-938d-abea69c56c9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !mmseqs createdb -h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "id": "81a04977-c06c-499f-bf53-a019bc0c84b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !wget https://raw.githubusercontent.com/mrzResearchArena/esm-embedding-clustering/main/Datasets/astral-SCOPe-K192.fa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "id": "70a04f3f-1123-4326-99b7-31a588233288",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "astral-SCOPe-K192.fa   astral-SCOPe-K192-numerical-header.fa\n",
      "astral-SCOPe-K192+.fa\n"
     ]
    }
   ],
   "source": [
    "!ls astral-SCOPe-K192*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "id": "590b134e-e3cc-4142-8772-af7b4c782a18",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !mmseqs easy-cluster -h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "id": "74faf9d6-7331-407c-891e-6efb0b60cc72",
   "metadata": {},
   "outputs": [],
   "source": [
    "C=0\n",
    "file = open(file='astral-SCOPe-K192+.fa', mode='w')\n",
    "for header, sequence in esm.data.read_fasta('astral-SCOPe-K192.fa'):\n",
    "    header = '+'.join(header.split())\n",
    "    # print(header)\n",
    "    file.write(f'{header}\\n')\n",
    "    file.write(f'{sequence}\\n')\n",
    "    C+=1\n",
    "    # break\n",
    "file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "id": "ce34433a-919d-4e63-a02c-128210240f65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'>d2rsda1+g.50.1.0'"
      ]
     },
     "execution_count": 419,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "header"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65f9ff01-c7d2-4b6f-be28-2c144a92e8cd",
   "metadata": {},
   "source": [
    "#### Examples:\n",
    "- mmseqs easy-cluster examples/DB.fasta result tmp\n",
    "  - result_rep_seq.fasta: Representatives\n",
    "  - result_all_seq.fasta: FASTA-like per cluster\n",
    "  - result_cluster.tsv:   Adjacency list\n",
    " \n",
    "- Important parameters: --min-seq-id, --cov-mode, -c\n",
    "\n",
    "#### Parameters:\n",
    "\n",
    "![params](images-JPG/mmseqs2-params.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3db6b94b-2c25-4220-b985-6d7c1303b0b0",
   "metadata": {},
   "source": [
    "### MMseqs2's easy-cluster and easy-linclust:\n",
    "\n",
    "- Unlike linclust clustering, these two algorithms, easy-cluster and easy-linclust, can build clusters directly from the protein sequences (i.e., single FASTA or multiple FASTA)\n",
    "\n",
    "- ESMFold used easy-cluster algorithm to expedite their experiment.\n",
    "\n",
    "- I love `easy-cluster` because it provides a minimal number of cluster centroids. Albeit the number of cluster centroids highly depends on hyper-parameters tunning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "id": "3e48b67f-90b8-4392-a468-e872b9362896",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !mmseqs createdb -h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "id": "81d4e2c5-182b-4564-b7b7-88d13a06f11d",
   "metadata": {},
   "outputs": [],
   "source": [
    "!mmseqs easy-cluster\\\n",
    "    astral-SCOPe-K192+.fa\\\n",
    "    clusterFiles temporary\\\n",
    "    --cov-mode 1\\\n",
    "    -c 0.80\\\n",
    "    --cluster-reassign 0\\\n",
    "    --max-iterations 1000\\\n",
    "    --cluster-steps 3\\\n",
    "    -v 0\n",
    "\n",
    "##################################\n",
    "\n",
    "# !mmseqs easy-linclust\\\n",
    "#     astral-SCOPe-K192+.fa\\\n",
    "#     clusterFilesY temporary\\\n",
    "#     --cov-mode 1\\\n",
    "#     -c 0.80\\\n",
    "#     --max-iterations 1000\\\n",
    "#     -v 3\n",
    "# #################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "id": "90f70f59-5faf-4a67-b93b-06efb7b29781",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "clusterFiles\t\t     clusterFilesX.2\n",
      "clusterFiles_all_seqs.fasta  clusterFilesX.3\n",
      "clusterFiles_cluster.tsv     clusterFilesX.4\n",
      "clusterFiles.dbtype\t     clusterFilesX.5\n",
      "clusterFiles_h\t\t     clusterFilesX.6\n",
      "clusterFiles_h.dbtype\t     clusterFilesX.7\n",
      "clusterFiles_h.index\t     clusterFilesX.dbtype\n",
      "clusterFiles.index\t     clusterFilesX.index\n",
      "clusterFiles.lookup\t     clusterFilesX.tsv\n",
      "clusterFiles_rep_seq.fasta   clusterFilesY_all_seqs.fasta\n",
      "clusterFiles.source\t     clusterFilesY_cluster.tsv\n",
      "clusterFilesX.0\t\t     clusterFilesY_rep_seq.fasta\n",
      "clusterFilesX.1\n"
     ]
    }
   ],
   "source": [
    "!ls clusterFiles*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "id": "ea6e8c55-d356-4ea0-aac8-d53bce7ba8a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !mmseqs linclust -h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "id": "dd6cd9f7-277c-4da7-ad13-c6059652f054",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Linclust"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "id": "0dd0c25f-09bd-4180-b2de-8661d511dc2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# --dbtype INT --> Database type 0: auto, 1: amino acid 2: nucleotides [0]\n",
    "# !mmseqs createdb astral-SCOPe-K192.fa embeddingFilesX --dbtype 1 -v 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "id": "7e2c2796-1f89-4847-8613-691ad31de0bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !mmseqs linclust embeddingFilesX clusterFilesX temporaryX -v 3 --cov-mode 1 -c 0.9 --min-seq-id 0.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "id": "bb234a4c-e497-485c-8c91-c042c72b31c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !mmseqs createtsv embeddingFilesX clusterFilesX clusterFilesX.tsv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "id": "f202a6db-25e6-4896-882f-ff1a713726bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !ls clusterFilesX*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "id": "a5377986-0cc6-48dc-a381-61cfe7b499b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !mmseqs linclust -h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "id": "8dab6249-f1b3-4949-b652-75f6c0302eb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# linclustTable = pd.read_csv('clusterFilesX.tsv', sep='\\t', header=None)\n",
    "# linclustTable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "id": "ad70d192-64bc-42a3-baa0-f19fe2191189",
   "metadata": {},
   "outputs": [],
   "source": [
    "# v1 = linclustTable[1]\n",
    "# v1 = np.array(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "id": "650f9ec9-668b-4aa6-a990-5b4a83f8f369",
   "metadata": {},
   "outputs": [],
   "source": [
    "# v1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "id": "3a896b52-12f6-4ef2-b3e4-6a57d9fa668c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# len(set(v1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "id": "0ad0b4db-6712-4b44-a523-8d7a45110f05",
   "metadata": {},
   "outputs": [],
   "source": [
    "# v2 = linclustTable[0]\n",
    "# # v1 = np.array(v)\n",
    "# len(set(v2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "id": "6099bfd9-31f1-4479-95a2-e92ec8de62fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# np.array(v2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "id": "12fbfe72-8e03-47d7-b588-75eb20eee053",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.metrics.cluster import v_measure_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "id": "4fe3103c-b960-4757-9936-0e59e49d7e37",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Y = np.load('/home/rafsanjani/backup-research/esm/originalLabels.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "id": "b207a778-529c-413f-9f29-9da78d193fb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "id": "9ac16deb-1fae-4715-aee8-24af1c5a0fdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !ls clusterFilesX*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "id": "1cc112da-1825-436a-bc49-6bbb4725503a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !cat clusterFilesX.0 | wc -l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "id": "5b24c8bf-7c93-4f3b-9d59-079c5ad63547",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !cat clusterFilesX.1 | wc -l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "id": "c76f59df-721d-41ef-96fa-9d6faa3dec07",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%bash\n",
    "\n",
    "# for i in {1..7..1}; do\n",
    "#     cat clusterFilesX.$i | wc -l\n",
    "# done\n",
    "\n",
    "\n",
    "# # for i in {1..7..1}; do\n",
    "# #     v=clusterFilesX.$i\n",
    "# #     echo $v\n",
    "# # done"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "id": "f2a80dd2-a0c6-4689-9842-ce45c3475800",
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(3142+3961+2589+5509+3557+0+2420) # The number of centriods for Linclust"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "id": "3180baf3-df6f-444e-b56a-12d0c04a28ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">d1gu2a_+a.3.1.1\n",
      ">d1gu2a_+a.3.1.1\n",
      "DVTNAEKLVYKYTNIAHSANPMYEAPSITDGKIFFNRKFKTPSGKEAACASCHTNNPANVGKNIVTGKEIPPLAPRVNTKRFTDIDKVEDEFTKHCNDILGADCSPSEKANFIAYLLTETKPTK\n",
      ">d155ca_+a.3.1.1\n",
      ">d155ca_+a.3.1.1\n",
      "NEGDAAKGEKEFNKCKACHMIQAPDGTDIKGGKTGPNLYGVVGRKIASEEGFKYGEGILEVAEKNPDLTWTEANLIEYVTDPKPLVKKMTDDKGAKTKMTFKMGKNQADVVAFLAQDDPDAXXXXXXXXXXXXX\n",
      ">d1cota_+a.3.1.1\n",
      "DGDAAKGEKEFNKCKACHMIQAPDGTDIIKGGKTGPNLYGVVGRKIASEEGFKYGEGILEVAEKNPDLTWTEADLIEYVTDPKPWLVKMTDDKGAKTKMTFKMGKNQADVVAFLAQNSPDA\n",
      ">d2bh4x_+a.3.1.1\n",
      "EGDAAKGEKEFNKCKACHMVQAPDGTDIVKGGKTGPNLYGVVGRKIASVEGFKYGDGILEVAEKNPDMVWSEADLIEYVTDPKPWLVEKTGDSAAKTKKTFKLGKNQADVVAFLAQHSPDAG\n",
      "cat: write error: Broken pipe\n"
     ]
    }
   ],
   "source": [
    "cat clusterFiles_all_seqs.fasta | head -10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "id": "add7adb4-98ce-43d5-925f-db5ac0fbd57c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">d1y3ta1+b.82.1.5 \n",
      "CTHSLPKEKMPYLLRSGEGERYLFGRQVATVMANGRSTGDLFEIVLLSGGKGDAFPLHVHKDTHEGILVLDGKLELTLDGERYLLISGDYANIPAGTPHSYRMQSHRTRLVSYTMKGNVAHLYSVIGNPYDHAEHPPYASEEVSNERFAEAAAVATIVFLDEAKPACSAKLAELTELPDGAVPYVLESGEGDRLLTGDQLHRIVAAQKNTDGQFIVVSSEGPKGDRIVDHYHEYHTETFYCLEGQMTMWTDGQEIQLNPGDFLHVPANTVHSYRLDSHYTKMVGVLVPGLFEPFFRTLGDPYEGHIFPCKPQALRFDRILQNIEALDLKV\n",
      ">d1juha_+b.82.1.5 \n",
      "SSLIVEDAPDHVRPYVIRHYSHARAVTVDTQLYRFYVTGPSSGYAFTLMGTNAPHSDALGVLPHIHQKHYENFYCNKGSFQLWAQSGNETQQTRVLSSGDYGSVPRNVTHTFQIQDPDTEMTGVIVPGGFEDLFYYLGTNATDTTHTPYIPSSSDSSSTTGPDSSTISTLQSFDVYAELSFTPRTDTVNGTAPANTVWHTGANALASTAGDPYFIANGWGPKYLNSQYGYQIVAPFVTATQAQDTNYTLSTISMSTTPSTVTVPTWSFPGACAFQVQEGRVVVQIGDYAATELGSGDVAFIPGGVEFKYYSEAYFSKVLFVSSGSDGLDQNLVNGGEEWSSVSFPADW\n",
      ">d1zrra_+b.82.1.6 \n",
      "SALTIFSVKDPQNSLWHSTNAEEIQQQLNAKGVRFERWQADRDLGAAPTAETVIAAYQHAIDKLVAEKGYQSWDVISLRADNPQKEALREKFLNEHTHGEDEVRFFVEGAGLFCLHIGDEVFQVLCEKNDLISVPAHTPHWFDMGSEPNFTAIRIFDNPEGWIAQFTGDDIASAYPRLA\n",
      ">d1vr3a1+b.82.1.6 \n",
      "MVQAWYMDESTADPRKPHRAQPDRPVSLEQLRTLGVLYWKLDADKYENDPELEKIRKMRNYSWMDIITICKDTLPNYEEKIKMFFEEHLHLDEEIRYILEGSGYFDVRDKEDKWIRISMEKGDMITLPAGIYHRFTLDEKNYVKAMRLFVGEPVWTPYNRPADHFDARVQYMSFLEGTA\n",
      ">d1x82a1+b.82.1.7 \n",
      "MYKEPFGVKVDFETGIIEGAKKSVRRLSDMEGYFVDERAWKELVEKEDPVVYEVYAVEQEEKEGDLNFATTVLYPGKVGKEFFFTKGHFHAKLDRAEVYVALKGKGGMLLQTPEGDAKWISMEPGTVVYVPPYWAHRTVNIGDEPFIFLAIYPADAGHDYGTIAEKGFSKIVIEENGEVKVVDNPRWKK\n",
      "cat: write error: Broken pipe\n"
     ]
    }
   ],
   "source": [
    "cat clusterFiles_rep_seq.fasta | head -10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "id": "2e721ba8-2d8f-44ba-97c1-9a0788c1e1de",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>d6wuea_+d.58.5.0</td>\n",
       "      <td>d6wuea_+d.58.5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>d6wuea_+d.58.5.0</td>\n",
       "      <td>d3dfea_+d.58.5.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>d6wuea_+d.58.5.0</td>\n",
       "      <td>d6mm2a1+d.58.5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>d1ju5a_+d.93.1.1</td>\n",
       "      <td>d1ju5a_+d.93.1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>d1ju5a_+d.93.1.1</td>\n",
       "      <td>d5jn0a_+d.93.1.1</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>24573</th>\n",
       "      <td>d1qxpa4+d.3.1.3</td>\n",
       "      <td>d1mdwa_+d.3.1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24574</th>\n",
       "      <td>d1qxpa4+d.3.1.3</td>\n",
       "      <td>d1tl9a_+d.3.1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24575</th>\n",
       "      <td>d1qxpa4+d.3.1.3</td>\n",
       "      <td>d1dkva4+d.3.1.0</td>\n",
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       "    <tr>\n",
       "      <th>24576</th>\n",
       "      <td>d1qxpa4+d.3.1.3</td>\n",
       "      <td>d1ziva1+d.3.1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24577</th>\n",
       "      <td>d1qxpa4+d.3.1.3</td>\n",
       "      <td>d6p3qa1+d.3.1.0</td>\n",
       "    </tr>\n",
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       "<p>24578 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      0                 1\n",
       "0      d6wuea_+d.58.5.0  d6wuea_+d.58.5.0\n",
       "1      d6wuea_+d.58.5.0  d3dfea_+d.58.5.0\n",
       "2      d6wuea_+d.58.5.0  d6mm2a1+d.58.5.0\n",
       "3      d1ju5a_+d.93.1.1  d1ju5a_+d.93.1.1\n",
       "4      d1ju5a_+d.93.1.1  d5jn0a_+d.93.1.1\n",
       "...                 ...               ...\n",
       "24573   d1qxpa4+d.3.1.3   d1mdwa_+d.3.1.3\n",
       "24574   d1qxpa4+d.3.1.3   d1tl9a_+d.3.1.3\n",
       "24575   d1qxpa4+d.3.1.3   d1dkva4+d.3.1.0\n",
       "24576   d1qxpa4+d.3.1.3   d1ziva1+d.3.1.3\n",
       "24577   d1qxpa4+d.3.1.3   d6p3qa1+d.3.1.0\n",
       "\n",
       "[24578 rows x 2 columns]"
      ]
     },
     "execution_count": 446,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = pd.read_csv('clusterFiles_cluster.tsv', sep='\\t', header=None)\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "id": "0cfb99fc-7dc3-49f6-89e3-99803b891f00",
   "metadata": {},
   "outputs": [],
   "source": [
    "def showD(d):\n",
    "    C = 1\n",
    "    for key, value in d.items():\n",
    "        print(f'{key}: {value}')\n",
    "        if C==10: break\n",
    "        C+=1\n",
    "    #end-for\n",
    "#end-def"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "id": "1a87db16-f326-4f8a-b5a7-822c72cbffb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0        d6wuea_+d.58.5.0\n",
      "1        d6wuea_+d.58.5.0\n",
      "2        d6wuea_+d.58.5.0\n",
      "3        d1ju5a_+d.93.1.1\n",
      "4        d1ju5a_+d.93.1.1\n",
      "               ...       \n",
      "24573     d1qxpa4+d.3.1.3\n",
      "24574     d1qxpa4+d.3.1.3\n",
      "24575     d1qxpa4+d.3.1.3\n",
      "24576     d1qxpa4+d.3.1.3\n",
      "24577     d1qxpa4+d.3.1.3\n",
      "Name: 0, Length: 24578, dtype: object\n",
      "4943\n"
     ]
    }
   ],
   "source": [
    "centroids = table[0]\n",
    "print(centroids)\n",
    "print(len(set(centroids)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "id": "d456adf8-c039-4606-aebe-a454ffb9eb50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0        d6wuea_+d.58.5.0\n",
      "1        d3dfea_+d.58.5.0\n",
      "2        d6mm2a1+d.58.5.0\n",
      "3        d1ju5a_+d.93.1.1\n",
      "4        d5jn0a_+d.93.1.1\n",
      "               ...       \n",
      "24573     d1mdwa_+d.3.1.3\n",
      "24574     d1tl9a_+d.3.1.3\n",
      "24575     d1dkva4+d.3.1.0\n",
      "24576     d1ziva1+d.3.1.3\n",
      "24577     d6p3qa1+d.3.1.0\n",
      "Name: 1, Length: 24578, dtype: object\n",
      "24578\n"
     ]
    }
   ],
   "source": [
    "sequenceIDs = table[1]\n",
    "sequenceIDs\n",
    "print(sequenceIDs)\n",
    "print(len(set(sequenceIDs)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "id": "5b7c860e-94c7-4554-943d-c20cc2c98130",
   "metadata": {},
   "outputs": [],
   "source": [
    "d = dict(zip(sequenceIDs, centroids))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 451,
   "id": "93d8e774-0186-4822-8205-f0aed8707565",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "d6wuea_+d.58.5.0: d6wuea_+d.58.5.0\n",
      "d3dfea_+d.58.5.0: d6wuea_+d.58.5.0\n",
      "d6mm2a1+d.58.5.0: d6wuea_+d.58.5.0\n",
      "d1ju5a_+d.93.1.1: d1ju5a_+d.93.1.1\n",
      "d5jn0a_+d.93.1.1: d1ju5a_+d.93.1.1\n",
      "d2rvfa1+d.93.1.0: d1ju5a_+d.93.1.1\n",
      "d2b3oa2+d.93.1.0: d1ju5a_+d.93.1.1\n",
      "d1bf5a3+d.93.1.1: d1bf5a3+d.93.1.1\n",
      "d1bg1a3+d.93.1.1: d1bf5a3+d.93.1.1\n",
      "d6tlca3+d.93.1.0: d1bf5a3+d.93.1.1\n"
     ]
    }
   ],
   "source": [
    "showD(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "id": "fa5215c0-28c1-4a7f-8c7c-014765c4b1e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24578"
      ]
     },
     "execution_count": 452,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(d.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "id": "b4891cfa-1f03-41d4-a70c-e4b9d237cf0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4943"
      ]
     },
     "execution_count": 453,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(d.values())) # It denotes number of centroids."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "id": "412f8e02-4fe5-4049-b5fe-73e8e890c101",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = {}\n",
    "for key, value in d.items():\n",
    "    sequenceID = key.split('+')[0]\n",
    "    superfamily = value.split('+')[1]\n",
    "    superfamily = '.'.join(superfamily.split('.')[0:-1])\n",
    "    \n",
    "    # print(sequenceID)\n",
    "    # print(superfamily)\n",
    "    \n",
    "    results[sequenceID] = superfamily\n",
    "    \n",
    "    # break\n",
    "#end-for"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "id": "d161ecd8-512e-4b42-b719-23b7087c1c0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "d6wuea_: d.58.5\n",
      "d3dfea_: d.58.5\n",
      "d6mm2a1: d.58.5\n",
      "d1ju5a_: d.93.1\n",
      "d5jn0a_: d.93.1\n",
      "d2rvfa1: d.93.1\n",
      "d2b3oa2: d.93.1\n",
      "d1bf5a3: d.93.1\n",
      "d1bg1a3: d.93.1\n",
      "d6tlca3: d.93.1\n"
     ]
    }
   ],
   "source": [
    "showD(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "id": "c4b71748-5ab1-4172-b2c0-09046be0fefb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24578"
      ]
     },
     "execution_count": 456,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(results.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "id": "53c9fdc5-ec54-401d-b16f-3e3a0f6e85f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "192"
      ]
     },
     "execution_count": 457,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(results.values()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "id": "c2788b9e-c893-4a38-a6dc-e4342eb1cf51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a.1.1\n",
      "g.50.1\n"
     ]
    }
   ],
   "source": [
    "print(results['d1ux8a_'])\n",
    "print(results['d2e6sa1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "id": "1c7a633b-6d0d-4847-8cf9-3fb63eb921b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "C=1\n",
    "clusterResults = []\n",
    "for header, sequence in esm.data.read_fasta('astral-SCOPe-K192.fa'):\n",
    "    \n",
    "    header = header.split()[0][1:]\n",
    "    # print(header)\n",
    "    \n",
    "    clusterResults.append(results[header])\n",
    "    \n",
    "    # if C==10: break\n",
    "    # C+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "id": "357947c1-fad1-41eb-9f24-135b51dc19fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# clusterResults"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "id": "a4b5b873-dad3-49ce-a243-83783030555a",
   "metadata": {},
   "outputs": [],
   "source": [
    "C=1\n",
    "origin = []\n",
    "for header, _ in esm.data.read_fasta('astral-SCOPe-K192.fa'):\n",
    "    \n",
    "    header = '.'.join(header.split()[1].split('.')[:-1])\n",
    "    origin.append(header)\n",
    "    \n",
    "    # if C==10: break\n",
    "    # C+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 462,
   "id": "2b854e26-0bbd-4087-ac86-8b022381eede",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24565\n"
     ]
    }
   ],
   "source": [
    "C = 0\n",
    "for y, yp in zip(origin, clusterResults):\n",
    "    if y == yp:\n",
    "        C += 1\n",
    "print(C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "id": "e0dddbaa-8cd7-4f8e-8c44-8492d91b9e88",
   "metadata": {},
   "outputs": [],
   "source": [
    "# clusterResults"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "id": "59fec928-39af-4015-a05f-720374cd9646",
   "metadata": {},
   "outputs": [],
   "source": [
    "# clusterResults"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "id": "eee540c4-efc1-4ab6-8238-f1da744c107e",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(arr=clusterResults, file='outputMMseqs2-easy-cluster')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "id": "9a5d3eee-7b95-44b1-81f9-ff9abefac7e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "Yp = np.load('outputMMseqs2-easy-cluster.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "id": "522db9a4-5b34-4b33-a4f5-2367df849623",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['a.1.1', 'a.1.1', 'a.1.1', 'a.1.1', 'a.1.1', 'a.1.1', 'a.1.1',\n",
       "       'a.1.1', 'a.1.1', 'a.1.1'], dtype='<U7')"
      ]
     },
     "execution_count": 468,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Yp[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "id": "4f2263fa-0f31-4b51-9288-bdb81fae364d",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = np.load('originalLabelsAlpha.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "id": "dedac72b-fb68-4c50-a658-1d4e794c09aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['a.1.1', 'a.1.1', 'a.1.1', ..., 'g.50.1', 'g.50.1', 'g.50.1'],\n",
       "      dtype='<U7')"
      ]
     },
     "execution_count": 471,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "id": "d1b6ea2d-6b05-4592-9b55-b7728b9107da",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics.cluster import v_measure_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 473,
   "id": "24bd16b9-32e2-48c6-b93e-aa10503b00c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99.96\n"
     ]
    }
   ],
   "source": [
    "print(f'{v_measure_score(labels_true=Y, labels_pred=Yp)*100.0:0.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 475,
   "id": "83d2ca72-f1c2-4bbf-a841-cef94f08ebb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# C=0\n",
    "# for sequenceID, centroid in zip(sequenceIDs, centroids):\n",
    "#     if sequenceID == centroid:\n",
    "#         C+=1\n",
    "#     #end-for\n",
    "# #end-for\n",
    "# print(f'Matched: {C} of {len(sequenceIDs)}')\n",
    "# print(f'{(C/len(sequenceIDs))*100.0:.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "id": "aac09f99-b9ea-4839-96c1-31b00d41ad6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# centroids = np.array(centroids)\n",
    "# print(centroids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "id": "522a6395-3c5c-40c4-bf2b-01d1c31a8452",
   "metadata": {},
   "outputs": [],
   "source": [
    "# sequenceIDs = np.array(sequenceIDs)\n",
    "# print(sequenceIDs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "id": "debb115d-2464-4643-895b-c2f5f3b78d1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# d = dict(zip(sequenceIDs, centroids))\n",
    "# # print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "id": "7107dcfc-106e-4406-b76e-e25755e5adea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# len(set(d.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 480,
   "id": "5c2982cd-77e3-4de5-a1ef-98eae02221ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# len(set(d.values()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "id": "12a97fa3-0de2-4fe8-96c0-f42d128228b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# C=0\n",
    "# for header, sequence in esm.data.read_fasta('astral-SCOPe-K192.fa'):\n",
    "#     print(header)\n",
    "#     break\n",
    "# #for-end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "id": "8c28e2e6-ba98-4e70-8143-239f1a7afb82",
   "metadata": {},
   "outputs": [],
   "source": [
    "# V = []\n",
    "# for centroid in centroids:\n",
    "#     # V.append(centroid.split('+')[1])\n",
    "#     print(centroid.split('+')[1])\n",
    "# #end-for"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "id": "ede7f889-ce0e-4cbc-a448-634ba88ae486",
   "metadata": {},
   "outputs": [],
   "source": [
    "# V[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "id": "05ab5249-87dd-4001-bb61-e3d4c5e96fad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# centroids = V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f584b850-3e49-442c-935c-cd8ccdc43519",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b92a3742-905b-4a9c-9f3d-bc406859b6cc",
   "metadata": {},
   "source": [
    "### Re-programmed with numerical header:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 485,
   "id": "93b451de-4e93-4249-b1c2-fadc32c30061",
   "metadata": {},
   "outputs": [],
   "source": [
    "# C=0\n",
    "# file = open(file='astral-SCOPe-K192-numerical-header.fa', mode='w')\n",
    "# for header, sequence in esm.data.read_fasta('astral-SCOPe-K192.fa'):\n",
    "#     file.write(f'>{C}\\n')\n",
    "#     file.write(f'{sequence}\\n')\n",
    "#     C+=1\n",
    "#     # break\n",
    "# file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 486,
   "id": "421c3515-98a8-48d2-8087-3eadeb4140a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# C=0\n",
    "# file = open(file='astral-SCOPe-K192-numerical-header.fa', mode='w')\n",
    "# for header, sequence in esm.data.read_fasta('astral-SCOPe-K192.fa'):\n",
    "#     file.write(f'>{C}\\n')\n",
    "#     file.write(f'{sequence}\\n')\n",
    "#     C+=1\n",
    "#     # break\n",
    "# file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 487,
   "id": "216b1802-fafc-4a1c-80d2-1a7f2c604566",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !ls astral-SCOPe-K192*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "id": "ee2d08b2-4e83-4079-a418-1c3c96492856",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !mmseqs easy-cluster \\\n",
    "#     astral-SCOPe-K192-numerical-header.fa\\\n",
    "#     clusterFiles temporary\\\n",
    "#     --cov-mode 1\\\n",
    "#     -c 0.80\\\n",
    "#     --cluster-reassign 0\\\n",
    "#     --max-iterations 5000\\\n",
    "#     --cluster-steps 9\\\n",
    "#     -v 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 489,
   "id": "ca3449f3-1a02-4648-867e-8df49bf85534",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !ls clusterFiles*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "id": "6b385e50-e964-4dff-82bb-950f41c44ff6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# cat clusterFiles_all_seqs.fasta | head -10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "id": "91eb8373-4d7f-4ec7-abf8-efcc7a38d08d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# table = pd.read_csv('clusterFiles_cluster.tsv', sep='\\t', header=None)\n",
    "# table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "id": "199fdb8c-2a45-4f18-a54e-48355fdfc0c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# centroids = table[0]\n",
    "# centroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "id": "c440bace-c8ed-4c1a-bf18-4566fabdb7be",
   "metadata": {},
   "outputs": [],
   "source": [
    "# w = np.array(centroids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "id": "630f0c48-4022-4d62-b08a-0e4a0d4ea1be",
   "metadata": {},
   "outputs": [],
   "source": [
    "# len(set(w))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "id": "0917feb3-7ee0-41ac-9952-65cfcfae986b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# sequenceIDs = table[1]\n",
    "# sequenceIDs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "id": "48c1c50b-c6de-4a38-97bf-a478532eb794",
   "metadata": {},
   "outputs": [],
   "source": [
    "# C=0\n",
    "# for sequenceID, centroid in zip(sequenceIDs, centroids):\n",
    "#     if sequenceID == centroid:\n",
    "#         C+=1\n",
    "#     #end-for\n",
    "# #end-for"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "id": "fa97ea01-b904-40b8-9e06-067f5b73d536",
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(f'Matched: {C} of {len(sequenceIDs)}')\n",
    "# print(f'{(C/len(sequenceIDs))*100.0:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb0bad00-13a3-4e13-bcca-36a950cb12d2",
   "metadata": {},
   "source": [
    "### Optional: Performance\n",
    "\n",
    "- It testifies to the retrieval of superfamilies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 498,
   "id": "f4aa1157-77db-43a8-9599-34655c221b5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import esm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "id": "ba114c1e-a366-428e-9ec4-be470434be1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "datasetLocation = 'astral-SCOPe-K192.fa'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 500,
   "id": "ed0b06ad-de27-4bf6-ae67-d5694aba168c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# C=1\n",
    "superfamilies = []\n",
    "# loopUpTable = {}\n",
    "seqIDs = []\n",
    "for header, _seq in esm.data.read_fasta(datasetLocation):\n",
    "    seqIDs.append(header.split()[0][1:])\n",
    "    superfamily = '.'.join(header.split()[1].split('.')[0:-1])\n",
    "    superfamilies.append(superfamily)\n",
    "    \n",
    "    # print(header.split()[0][1:])\n",
    "    # print(superfamily)\n",
    "    \n",
    "    # loopUpTable[]\n",
    "    \n",
    "    # break\n",
    "    \n",
    "    # if C==10: break\n",
    "    # C+=1\n",
    "#end-for"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 501,
   "id": "e9180a78-e1a7-4f43-800d-674e9a9cb90e",
   "metadata": {},
   "outputs": [],
   "source": [
    "inLookUpTable = dict(zip(seqIDs, superfamilies))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 502,
   "id": "df9e818d-e2a5-4864-a5fe-11635678952b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# inLookUpTable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 503,
   "id": "905f604d-c854-47d9-9df6-8ffa3db6cfaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# superfamilies[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 504,
   "id": "0bbcdc14-ac8a-487e-bf2c-72d87001a6ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Y = superfamilies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "id": "f1bd2a43-054e-42a6-a4b8-0c9126032da6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !mmseqs easy-cluster \\\n",
    "#     astral-SCOPe-K192.fa\\\n",
    "#     clusterFiles temporary\\\n",
    "#     --cov-mode 1\\\n",
    "#     -c 0.80\\\n",
    "#     --cluster-reassign 0\\\n",
    "#     --max-iterations 5000\\\n",
    "#     -v 0\n",
    "\n",
    "!mmseqs easy-linclust \\\n",
    "    astral-SCOPe-K192.fa\\\n",
    "    clusterFiles temporary\\\n",
    "    --cov-mode 1\\\n",
    "    -c 0.80\\\n",
    "    --max-iterations 5000\\\n",
    "    -v 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 506,
   "id": "fdddf653-5b0c-4018-a8b0-1aa9cfc2347c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "clusterFiles_cluster.tsv\n"
     ]
    }
   ],
   "source": [
    "!ls clusterFiles_cluster*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "id": "3d7055fc-0ba4-47ba-a95b-60fe55655c04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>d1j1ta_</td>\n",
       "      <td>d1j1ta_</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>d1vava_</td>\n",
       "      <td>d1vava_</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>d1uaia_</td>\n",
       "      <td>d1uaia_</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>d1utwa2</td>\n",
       "      <td>d1utwa2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>d1y4wa1</td>\n",
       "      <td>d1y4wa1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24573</th>\n",
       "      <td>d3cqla_</td>\n",
       "      <td>d3cqla_</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24574</th>\n",
       "      <td>d4msta_</td>\n",
       "      <td>d4msta_</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24575</th>\n",
       "      <td>d4tx7a_</td>\n",
       "      <td>d4tx7a_</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24576</th>\n",
       "      <td>d1f6ra_</td>\n",
       "      <td>d1f6ra_</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24577</th>\n",
       "      <td>d1f6ra_</td>\n",
       "      <td>d3b0ka1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24578 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             0        1\n",
       "0      d1j1ta_  d1j1ta_\n",
       "1      d1vava_  d1vava_\n",
       "2      d1uaia_  d1uaia_\n",
       "3      d1utwa2  d1utwa2\n",
       "4      d1y4wa1  d1y4wa1\n",
       "...        ...      ...\n",
       "24573  d3cqla_  d3cqla_\n",
       "24574  d4msta_  d4msta_\n",
       "24575  d4tx7a_  d4tx7a_\n",
       "24576  d1f6ra_  d1f6ra_\n",
       "24577  d1f6ra_  d3b0ka1\n",
       "\n",
       "[24578 rows x 2 columns]"
      ]
     },
     "execution_count": 507,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = pd.read_csv('clusterFiles_cluster.tsv', sep='\\t', header=None)\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "id": "5f2d0403-2cb7-4b5d-b0de-e6341840f721",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21116"
      ]
     },
     "execution_count": 508,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Cs = table[0]\n",
    "len(set(Cs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "id": "a87b464a-1e44-43c9-8054-9f127762a598",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24578"
      ]
     },
     "execution_count": 509,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seqIDs = table[1]\n",
    "len(set(seqIDs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 510,
   "id": "61543fca-45b0-476c-acb4-0bc9b08e7300",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['d1j1ta_' 'd1vava_' 'd1uaia_' ... 'd4tx7a_' 'd1f6ra_' 'd1f6ra_']\n",
      "['d1j1ta_' 'd1vava_' 'd1uaia_' ... 'd4tx7a_' 'd1f6ra_' 'd3b0ka1']\n"
     ]
    }
   ],
   "source": [
    "Cs = np.array(Cs)\n",
    "print(Cs)\n",
    "\n",
    "seqIDs = np.array(seqIDs)\n",
    "print(seqIDs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "id": "38641e9d-1f30-448a-9d96-6790f252d2ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21116\n"
     ]
    }
   ],
   "source": [
    "C=0\n",
    "for c, seqID in zip(Cs, seqIDs):\n",
    "    if c==seqID:\n",
    "        C+=1\n",
    "print(C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 512,
   "id": "79c5aa3b-ba86-45f5-853d-762910a618ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "outLookUpTable = dict(zip(seqIDs, Cs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 513,
   "id": "c243d620-aeed-4aa0-8d5c-b5cde28f9c2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# outLookUpTable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 514,
   "id": "d563658b-30ea-4408-86e3-7ca399ef0614",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21116\n"
     ]
    }
   ],
   "source": [
    "C=0\n",
    "for key, value in outLookUpTable.items():\n",
    "    if key == value:\n",
    "        C+=1\n",
    "print(C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 515,
   "id": "9fc6b6e0-36fb-424a-a00c-c3ebfab82b7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "modifiedOutLookUpTable = {}\n",
    "for key, value in outLookUpTable.items():\n",
    "    modifiedOutLookUpTable[key] = inLookUpTable[value]\n",
    "\n",
    "# {'d2qy0a2': 'd2qy0a2',\n",
    "#  'd1md8a2': 'd2qy0a2',\n",
    "#  'd5ubmb2': 'd2qy0a2',\n",
    "#  'd1elva2': 'd2qy0a2',\n",
    "#  'd3gova2': 'd2qy0a2',..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 516,
   "id": "7ddfcb24-3bfe-4900-be97-a6a954300cf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# modifiedOutLookUpTable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 517,
   "id": "5ff654d2-6075-4759-97ee-772015f673fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24576\n"
     ]
    }
   ],
   "source": [
    "C = 0\n",
    "for header, _seq in esm.data.read_fasta(datasetLocation):\n",
    "    seqID = header.split()[0][1:]\n",
    "    # print(seqID)\n",
    "    y  = inLookUpTable[seqID] \n",
    "    yp = modifiedOutLookUpTable[seqID]\n",
    "    \n",
    "    if y == yp:\n",
    "        C+=1\n",
    "    #end-if\n",
    "#end-for\n",
    "\n",
    "print(C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 518,
   "id": "1e49bc85-cca1-4595-9713-f00975616329",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 519,
   "id": "7fd6c885-27a2-43c0-9881-55562dd4eef7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# modifiedOutLookUpTable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 520,
   "id": "65cb153e-fe58-421d-ae72-3d0554c5caf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "192"
      ]
     },
     "execution_count": 520,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(modifiedOutLookUpTable.values()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "id": "b2550ff2-743b-466e-b59b-c41e36f05a44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24578"
      ]
     },
     "execution_count": 521,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(modifiedOutLookUpTable.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 522,
   "id": "a2de4820-be6f-4ffb-8302-3d87e3b6ba86",
   "metadata": {},
   "outputs": [],
   "source": [
    "counter = {}\n",
    "for key, value in modifiedOutLookUpTable.items():\n",
    "    # print(key, value)\n",
    "    # print(type(key))\n",
    "    # break\n",
    "    if value not in counter:\n",
    "        counter[value] = 1\n",
    "    else:\n",
    "        counter[value] += 1\n",
    "    # break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 523,
   "id": "c669333a-cf48-455c-a112-e69b8b1dd4b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 524,
   "id": "51d723cc-e628-4f75-bbc6-8f198b9a1261",
   "metadata": {},
   "outputs": [],
   "source": [
    "counter =sorted(counter.items(), key=lambda x: x[1], reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 525,
   "id": "6b520432-0772-426f-916b-456634ffed54",
   "metadata": {},
   "outputs": [],
   "source": [
    "# counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 526,
   "id": "0cac99f8-d79d-4204-befa-bdba2d74e477",
   "metadata": {},
   "outputs": [],
   "source": [
    "superfamilies = []\n",
    "members = []\n",
    "for v in counter:\n",
    "    superfamilies.append(v[0])\n",
    "    members.append(v[1])\n",
    "#end-for"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 528,
   "id": "4f945564-e869-47f0-8183-6d743e0fe82a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig = plt.figure(figsize = (25, 15))\n",
    "plt.bar(superfamilies[:15], members[:15], width = 0.4, color=(0.2, 0.4, 0.6, 0.6),  edgecolor='blue')\n",
    "plt.xlabel('Superfamilies', fontweight='bold', fontsize=15)\n",
    "plt.ylabel('Frequency (number of proteins)', fontweight='bold', fontsize=15)\n",
    "plt.title('This bar plot shows the number of proteins contains in the corresponding superfamily (after MMseqs2\\'s easy-cluster).', fontsize=22)\n",
    "plt.savefig(f'images-PNG/output-easy-cluster-15.png')\n",
    "plt.savefig(f'images-JPG/output-easy-cluster-15.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 529,
   "id": "27b7de2a-70a0-4476-a5cd-1c6391b38145",
   "metadata": {},
   "outputs": [],
   "source": [
    "# C = 1\n",
    "# for i in range(15, len(members), 15):\n",
    "#     fig = plt.figure(figsize = (25, 15))\n",
    "#     plt.bar(superfamilies[i-15:i], members[i-15:i], width = 0.4, color=(0.2, 0.4, 0.6, 0.6),  edgecolor='blue')\n",
    "#     plt.xlabel('Superfamilies', fontweight='bold', fontsize=15)\n",
    "#     plt.ylabel('Frequency (number of proteins)', fontweight='bold', fontsize=15)\n",
    "#     plt.title('This bar plot shows the number of proteins contains in the corresponding superfamily.', fontsize=22)\n",
    "#     plt.savefig(f'images-PNG/{(i+1)-15}-{i}.png')\n",
    "#     plt.savefig(f'images-JPG/{(i+1)-15}-{i}.jpg')\n",
    "#     if C==5: break\n",
    "#     C+=1\n",
    "#     # plt.show()\n",
    "# #end-for"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 530,
   "id": "fa6dd534-538a-4ac9-957a-49995a12ca67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['b.1.1',\n",
       " 'c.37.1',\n",
       " 'c.2.1',\n",
       " 'c.47.1',\n",
       " 'a.4.5',\n",
       " 'd.144.1',\n",
       " 'c.1.8',\n",
       " 'c.94.1',\n",
       " 'c.67.1',\n",
       " 'c.69.1',\n",
       " 'b.29.1',\n",
       " 'c.66.1',\n",
       " 'a.1.1',\n",
       " 'd.58.7',\n",
       " 'b.47.1']"
      ]
     },
     "execution_count": 530,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# members\n",
    "superfamilies[:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 531,
   "id": "edfaa49c-875e-485d-b4a3-efa4fa486b62",
   "metadata": {},
   "outputs": [],
   "source": [
    "C = 1\n",
    "\n",
    "d = {}\n",
    "for header, sequence in esm.data.read_fasta(datasetLocation):\n",
    "    header = '.'.join(header.split()[1].split('.')[0:-1])\n",
    "    \n",
    "    # print(header)\n",
    "    \n",
    "    if header not in d:\n",
    "        d[header] = 1\n",
    "    else:\n",
    "        d[header] += 1\n",
    "    \n",
    "    # print()\n",
    "    # if C==10: break\n",
    "    # C += 1\n",
    "#end-for"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 532,
   "id": "fe0d8cfb-ecf2-44f4-b5e2-c2df87dd1954",
   "metadata": {},
   "outputs": [],
   "source": [
    "originalCounter =sorted(d.items(), key=lambda x: x[1], reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 533,
   "id": "a9dbb8fc-155b-458b-9418-0d00559ae881",
   "metadata": {},
   "outputs": [],
   "source": [
    "# originalCounter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 534,
   "id": "d3008c11-4766-4ced-8843-ac45ba43c9bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "originalSuperfamilies = []\n",
    "originalMembers = []\n",
    "for v in originalCounter:\n",
    "    originalSuperfamilies.append(v[0])\n",
    "    originalMembers.append(v[1])\n",
    "#end-for"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 539,
   "id": "0aaa8d5d-4e15-4d0c-8fcb-64dd93416eaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fig = plt.figure(figsize = (25, 15))\n",
    "plotdata = pd.DataFrame({\n",
    "    \"before cluster (initial stage)\": originalMembers[:15],\n",
    "    \"after cluster (MMseqs2\\'s easy-cluster)\": members[:15]},\n",
    "    index=superfamilies[:15]\n",
    ")\n",
    "\n",
    "plotdata.plot(kind=\"bar\",figsize=(25, 15), color={'after cluster (MMseqs2\\'s easy-cluster)': (0.2, 0.4, 0.6, 0.6), 'before cluster (initial stage)': 'red'})\n",
    "# (0.2, 0.4, 0.6, 0.6)\n",
    "\n",
    "\n",
    "# index=[\"Django\", \"Gafur\", \"Tommy\", \"Ronnie\"])\n",
    "#superfamilies[:15], members[:15]\n",
    "\n",
    "# color=(0.2, 0.4, 0.6, 0.6)\n",
    "\n",
    "plt.xlabel('Superfamilies', fontweight='bold', fontsize=15)\n",
    "plt.ylabel('Frequency (number of proteins)', fontweight='bold', fontsize=15)\n",
    "plt.title('This bar plot shows the number of proteins contains in the corresponding superfamily.', fontsize=22)\n",
    "plt.savefig(f'images-PNG/compared-easy-cluster-15.png')\n",
    "plt.savefig(f'images-JPG/compared-easy-cluster-15.jpg')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 540,
   "id": "e11a4a02-715b-4e35-8252-449bfe68ca2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# help(plotdata.plot)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de0d2730-f7c0-4cbb-b5d6-bd4c6e3a21e8",
   "metadata": {},
   "source": [
    "### All superfamilies compared (MMseqs2/Linclust):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 541,
   "id": "0331cb0d-6990-4546-843f-2e361a06c87d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fig = plt.figure(figsize = (25, 15))\n",
    "plotdata = pd.DataFrame({\n",
    "    \"before cluster (initial stage)\": originalMembers[:],\n",
    "    \"after cluster (MMseqs2\\'s easy-cluster)\": members[:]},\n",
    "    index=range(len(superfamilies[:]))\n",
    ")\n",
    "\n",
    "plotdata.plot(kind=\"bar\",figsize=(25, 15), color={'after cluster (MMseqs2\\'s easy-cluster)': (0.2, 0.4, 0.6, 0.6), 'before cluster (initial stage)': 'red'})\n",
    "# (0.2, 0.4, 0.6, 0.6)\n",
    "\n",
    "\n",
    "# index=[\"Django\", \"Gafur\", \"Tommy\", \"Ronnie\"])\n",
    "#superfamilies[:15], members[:15]\n",
    "\n",
    "# color=(0.2, 0.4, 0.6, 0.6)\n",
    "\n",
    "plt.xlabel('Superfamilies', fontweight='bold', fontsize=15)\n",
    "plt.ylabel('Frequency (number of proteins)', fontweight='bold', fontsize=15)\n",
    "plt.title('This bar plot shows the number of proteins contains in the corresponding superfamily.', fontsize=22)\n",
    "plt.savefig(f'images-PNG/all-compared-easy-cluster.png')\n",
    "plt.savefig(f'images-JPG/all-compared-easy-cluster.jpg')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "58d210e9-3c24-495a-8ca6-7347035d2a01",
   "metadata": {},
   "outputs": [],
   "source": [
    "# help(plt.savefig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45915c82-baf5-4120-b587-fbae07531d50",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}